Comparison of different spectrum sensing methods.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Chang",slug:"aileen-y.-chang",email:"chang@email.gwu.edu",position:null,institution:null},{id:"342718",title:"Dr.",name:"Evelyn",middleName:null,surname:"Mendoza-Torres",fullName:"Evelyn Mendoza-Torres",slug:"evelyn-mendoza-torres",email:"evelyn.mendozat@unilibre.edu.co",position:null,institution:null},{id:"427633",title:"Dr.",name:"Franklin",middleName:null,surname:"Torres",fullName:"Franklin Torres",slug:"franklin-torres",email:"dummy+427633@intechopen.com",position:null,institution:null},{id:"427634",title:"Dr.",name:"Wendy",middleName:null,surname:"Rosales-Rada",fullName:"Wendy Rosales-Rada",slug:"wendy-rosales-rada",email:"dummy+427634@intechopen.com",position:null,institution:null},{id:"427635",title:"Dr.",name:"Liliana",middleName:null,surname:"Encinales",fullName:"Liliana Encinales",slug:"liliana-encinales",email:"dummy+427635@intechopen.com",position:null,institution:null},{id:"427636",title:"Dr.",name:"Lil",middleName:null,surname:"Avendaño",fullName:"Lil Avendaño",slug:"lil-avendano",email:"dummy+427636@intechopen.com",position:null,institution:null},{id:"427637",title:"Dr.",name:"María Fernanda",middleName:null,surname:"Pérez",fullName:"María Fernanda Pérez",slug:"maria-fernanda-perez",email:"dummy+427637@intechopen.com",position:null,institution:null},{id:"427638",title:"Dr.",name:"Ivana",middleName:null,surname:"Terán",fullName:"Ivana Terán",slug:"ivana-teran",email:"dummy+427638@intechopen.com",position:null,institution:null},{id:"427639",title:"Dr.",name:"David",middleName:null,surname:"Vergara",fullName:"David Vergara",slug:"david-vergara",email:"dummy+427639@intechopen.com",position:null,institution:null},{id:"427640",title:"Dr.",name:"Estefanie",middleName:null,surname:"Osorio-Llanes",fullName:"Estefanie Osorio-Llanes",slug:"estefanie-osorio-llanes",email:"dummy+427640@intechopen.com",position:null,institution:null},{id:"427641",title:"Dr.",name:"Paige",middleName:null,surname:"Fierbaugh",fullName:"Paige Fierbaugh",slug:"paige-fierbaugh",email:"dummy+427641@intechopen.com",position:null,institution:null},{id:"427642",title:"Dr.",name:"Wendy",middleName:null,surname:"Villamizar",fullName:"Wendy Villamizar",slug:"wendy-villamizar",email:"dummy+427642@intechopen.com",position:null,institution:null},{id:"457495",title:"Dr.",name:"Jairo",middleName:null,surname:"Castellar-Lopez",fullName:"Jairo Castellar-Lopez",slug:"jairo-castellar-lopez",email:"dummy+427643@intechopen.com",position:null,institution:null}]},book:{id:"10707",title:"Primary Health Care",subtitle:null,fullTitle:"Primary Health Care",slug:"primary-health-care",publishedDate:"March 16th 2022",bookSignature:"Ayşe Emel Önal",coverURL:"https://cdn.intechopen.com/books/images_new/10707.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"25840",title:"Prof.",name:"Ayse Emel",middleName:null,surname:"Onal",slug:"ayse-emel-onal",fullName:"Ayse Emel Onal"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"11518",leadTitle:null,title:"The Acoustics of Materials - New Approaches",subtitle:null,reviewType:"peer-reviewed",abstract:"
\r\n\tThis book should describe in detail sound propagation, process, and characteristics, hearing, and process of speech communication, sound absorption, noise acceptance, the fundamental process of acoustic and how the workplace can be designed to control the surrounding sound and its effects on workers. Use theory and possible practical application to drive the knowledge from human involvement in workplace activities to any possible risk of health and safety hazards of the job.
",isbn:"978-1-80356-651-1",printIsbn:"978-1-80356-650-4",pdfIsbn:"978-1-80356-652-8",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"769f942393275479acca64e4f4fea958",bookSignature:"Dr. Bankole Kolawole Fasanya and Dr. Sridhar Krishnamurti",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11518.jpg",keywords:"Frequency, Sound Power, Absorption, Noise, Soundproof, Reflection, Inverse Square, Perception, Signal, Background Noise, Building, Noise Barrier",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 18th 2022",dateEndSecondStepPublish:"May 26th 2022",dateEndThirdStepPublish:"July 25th 2022",dateEndFourthStepPublish:"October 13th 2022",dateEndFifthStepPublish:"December 12th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Fasanya is an Assistant Professor at Purdue University, USA. Prior to his current position, he has worked in different capacities with different institutions: Senior research associate (Auditory Protection and Prevention - US Army Aeromedical Research Laboratory, Adjunct Assistant Professor-NCAT, Facilities Engineer MVA, etc). Dr. Fasanya holds a Ph.D. in Industrial and systems engineering with a specialization in ergonomics and human factors.",coeditorOneBiosketch:"Dr. Sridhar Krishnamurti is a Professor and Program Director of Audiology at Auburn University. Sridhar has\r\nauthored a book, journal articles, and book chapters in Audiology and Hearing Conservation. He\r\nis a recipient of several Research grant awards, including the 1999 New Investigator Research\r\nAward from the American Academy of Audiology and the 2011 Auburn University Alumni\r\nUndergraduate Teaching Excellence and 2012 Auburn University Faculty Research Awards.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"214494",title:"Dr.",name:"Bankole",middleName:"Kolawole",surname:"Fasanya",slug:"bankole-fasanya",fullName:"Bankole Fasanya",profilePictureURL:"https://mts.intechopen.com/storage/users/214494/images/system/214494.jpg",biography:"Bankole K. Fasanya received a BSc in Mechanical Engineering in 1999 from The Polytechnic Ibadan, Nigeria, his Master’s degree in Industrial and Systems Engineering from Morgan State University, Maryland, USA and his doctorate degree in Industrial and Systems Engineering specialized in ergonomics and human factors from North Carolina Agricultural and Technical State University, USA. His research focuses on human and environmental safety, ergonomics and human factors, auditory prevention and protection and noise assessment and control at workplaces. Dr. Fasanya is currently an assistant professor at Purdue University Northwest in Indiana, USA. He currently serves as one of the executive members of the American Hearing Conservative Association (NHCA). He is an OSHA-Authorized general industry safety train the trainer and a certified occupational hearing conservationist (COHC).",institutionString:"Purdue University Northwest",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Purdue University Northwest",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:{id:"466252",title:"Dr.",name:"Sridhar",middleName:null,surname:"Krishnamurti",slug:"sridhar-krishnamurti",fullName:"Sridhar Krishnamurti",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003RKaOOQA1/Profile_Picture_2022-04-08T11:15:28.jpg",biography:"Dr. Sridhar Krishnamurti is a Professor and Program Director of Audiology at Auburn University.\r\nHe has served on the research grants review panel for several agencies and journals including\r\nAlzheimer’s Association, DOD Hearing Restoration Research, Ear and Hearing, American\r\nJournal of Public Health, and Journal of the American Academy of Audiology. Sridhar\r\nKrishnamurti has served as the past-continuing education administrator for Audiology Special\r\nInterest Divisions 6-9 and a Fellow of the American Academy of Audiology. Sridhar has\r\nauthored a book, journal articles, and book chapters in Audiology and Hearing Conservation. He\r\nis a recipient of several Research grant awards, including the 1999 New Investigator Research\r\nAward from the American Academy of Audiology and the 2011 Auburn University Alumni\r\nUndergraduate Teaching Excellence and 2012 Auburn University Faculty Research Awards.\r\nSridhar is currently President of the Council of Au.D Programs and an Executive Council member\r\nfor the National Hearing Conservation Association. His research has been funded by Oak Ridge\r\nAssociated Universities (ORISE) program and CDC-NIOSH.",institutionString:"Auburn University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Auburn University",institutionURL:null,country:{name:"United States of America"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"429342",firstName:"Zrinka",lastName:"Tomicic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/429342/images/20008_n.jpg",email:"zrinka@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"7620",title:"Safety and Health for Workers",subtitle:"Research and Practical Perspective",isOpenForSubmission:!1,hash:"1233909d682e2cced428e1042fd40ad4",slug:"safety-and-health-for-workers-research-and-practical-perspective",bookSignature:"Bankole Fasanya",coverURL:"https://cdn.intechopen.com/books/images_new/7620.jpg",editedByType:"Edited by",editors:[{id:"214494",title:"Dr.",name:"Bankole",surname:"Fasanya",slug:"bankole-fasanya",fullName:"Bankole Fasanya"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10198",title:"Response Surface Methodology in Engineering Science",subtitle:null,isOpenForSubmission:!1,hash:"1942bec30d40572f519327ca7a6d7aae",slug:"response-surface-methodology-in-engineering-science",bookSignature:"Palanikumar Kayaroganam",coverURL:"https://cdn.intechopen.com/books/images_new/10198.jpg",editedByType:"Edited by",editors:[{id:"321730",title:"Prof.",name:"Palanikumar",surname:"Kayaroganam",slug:"palanikumar-kayaroganam",fullName:"Palanikumar Kayaroganam"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"62099",title:"Advanced Ceramic Materials Sintered by Microwave Technology",doi:"10.5772/intechopen.78831",slug:"advanced-ceramic-materials-sintered-by-microwave-technology",body:'\nHigh-temperature processes are required to consolidate ceramic powders, such as zirconia (Y-TZP), alumina, silicon carbide, and so on, in order to obtain full densification of the material. Sintering is a common material processing technique aimed at fulfilling this task. The fundamental principle behind sintering consists in the thermal activation of mass transfer mechanisms when exposing a powder compact, known as a “green” body, to a high-temperature process, at a dwell temperature below the melting point of the material. The main purpose of sintering is to obtain a dense and resistant body with properties as close as possible to those of a theoretical, fully dense solid. However, in some cases, sintering can also be employed to adjust some of the properties based on the performance requirements of the material by not reaching full consolidation, such as in porous materials.
\nTwo main types of sintering can be identified based on the nature of the process: liquid phase and solid phase. Even though the term liquid phase may suggest exceeding the melting point of the material, it is used to describe the addition of compounds with significantly lower melting points that aid in the consolidation of the main powder, which is regarded as the matrix phase and provides the main properties of the consolidated body. In this chapter, however, only solid phase sintering is considered.
\nCurrently, innovative sintering methods are being explored and studied in order to modify densification mechanisms that may improve the microstructure and mechanical properties of sintered materials and also is very important to reduce time fabrication of these materials. Two main stages have been recognized during the sintering process: densification and grain growth [1]. The main purpose for modifying sintering mechanisms is to obtain relative densities close to theoretical values, while maintaining a controlled, but limited, grain growth [2]. Also, the optimization of the process by reducing the sintering time to decrease energy consumption and/or increasing heating rates is an important aspect that is being considered [3]. As a consequence, in order to improve the sintering process, novel non-conventional sintering methods have been investigated and developed.
\nParticularly, microwave sintering represents an interesting opportunity at consolidating advanced ceramic materials with a reduced processing time and energy consumption by utilizing electromagnetic radiation to provide high-enough temperatures that allow full densification of the material. The most important advantages of microwave sintering against conventional sintering methods are listed as follows [4, 5]:
shorter sintering time and lower energy consumption;
higher heating rates can be used;
materials with a finer (nanometric) microstructure with a high degree of densification and enhanced mechanical properties may be obtained due to the densification mechanisms involved;
flexible due to the possibility of processing
This chapter reports on microwave material interaction, the basics of microwave processing, heating mechanisms, theoretical aspects in dielectric heating, and microwave systems for heating. The challenges in the field of microwave processing of advanced materials, such as zirconia and lithium aluminosilicate, have been discussed and studied from the point of view of different authors.
\nMicrowaves have been used since the 1960s for heating purposes, particularly for food- and water-based products. Industrially, the use of microwave energy has become increasingly important because it represents an alternative to traditional with high-temperature processes. For example, so far, it has been employed in wood drying, resin curing, and polymer synthesis. The growing interest in industrial microwave heating is due mostly to the reduction of production costs resulting from lower energy consumption and shorter processing times [6, 7, 8]. However, several aspects need to still be investigated as each material behaves differently in the presence of microwaves.
\nThe application of microwave heating has now expanded to material science and technology, beginning with process control and moving onto ceramic drying, powder calcination, and decomposition of gases with microwave plasma, in addition to powder synthesis [5]. Scientific interest on this powerful tool has been recorded in the study as there has been an increase of bibliographical entries for the term “microwaves” in the last decades because the applications of this technology have diversified enormously. In the last 25 years, research and development on the dielectric heating attributed to microwaves began with topics in chemical synthesis and material processing, such as reactive sintering of superconductors, magneto-resistors, nanomaterials production, vitreous phase formation, hydrothermal generation of zeolites, among others [9]. In this sense, one of the major areas for research and development of microwave heating involves sintering of ceramic powders [10, 11].
\nMicrowave sintering is considered a relatively new ceramic material processing technique that differs significantly from conventional sintering methods due to the nature of the heat transfer mechanisms involved. Hence, microwave sintering is classified as a non-conventional sintering technique. This method presents itself as a fast, economical, and flexible processing tool. Some of the most important advantages against conventional sintering systems include lower energy consumption and production costs, reduction of processing times, higher heating rates, and, in some cases, even an improvement in the physical properties of the consolidated material [6, 12]. As a consequence, scientific interest in this novel technique has been developed progressively.
\nIn a general sense, microwave sintering increases the densification of the material at lower dwell temperatures when compared to conventional sintering [13, 14], employing shorter times and less energy [15, 16], and resulting in an improvement of the microstructure and mechanical properties [17, 18].
\nThe first sinterability studies of ceramics by exposure to microwave energy were carried out on the so-called black ceramics, which are the compounds based on tungsten carbide (WC). Two of the main issues regarding sintering of these materials by conventional means are the high temperatures (>1500°C) and long dwell times that result in grain coarsening. For the first time, in 1991, J. P. Cheng showed that the WC/Co system could be sintered by microwave heating technology [19]. In his work, a commercial WC powder with a 6–12 mol% Co content was investigated, and an improvement in the mechanical properties was achieved when compared to conventional methods by utilizing sintering temperature between 1250 and 1320°C and dwell times of only 10–30 min. The relative density values were close to theoretical and a fine and homogeneous microstructure was observed, without the use of grain growth inhibitors. Also, the materials exhibited a higher resistance to corrosion and erosion [20].
\nThe next step involved the processing of more traditional ceramic materials such as alumina and zirconia. Even though alumina behaves as a transparent material in the presence of microwaves, susceptors, which are materials with a high microwave absorbance, or dopants can be employed. Tian et al. were able to obtain 99.9% relative density values with an average grain size of 1.9 μm for MgO-doped Al2O3 sintered at 1700°C in a microwave oven [21]. Additionally, Katz and Blake were able to reach a densification of 99% for α-alumina with grain sizes between 5 and 50 μm after microwave sintering, where the total processing time was 100 min at a dwell temperature of 1400°C [22]. Transparent alumina materials have also been obtained via microwave processing at lower sintering temperature and shorter times [23].
\nIn the case of nanometric yttria-stabilized zirconia (YSZ), microstructure and mechanical properties can be enhanced when processed via microwave sintering [24]. By application of hybrid heating with the aid of a susceptor, sintered materials with densities close to theoretical values can be obtained at temperatures 200°C below those employed in conventional sintering [25, 26]. Moreover, the grain size decreases considerably and hardness values are almost 2 GPa higher [18].
\nIn the last 5 years, research on microwave sintering has also focused in the processing of ceramic composites to improve their functional as well as structural properties and extend its applications to several industrial sectors. Also, the design and optimization of current microwave ovens has also been an important research topic. These systems need to be adjusted to the characteristics of the material that is to be processed, since the behavior under a microwave field varies from one to another. Therefore, studying the fundamental principles and involved mechanisms in microwave energy conversion may allow the production of more energy-efficient ovens.
\nMicrowaves are a form of electromagnetic radiation that correspond to frequencies between 300 MHz (
Electromagnetic spectrum diagram.
Microwaves, as any other type of electromagnetic radiation, have electrical and magnetic field components, amplitude, phase angle, and the ability to propagate, that is, to transfer energy from one point to another. These properties govern the interaction of microwaves with materials and produce heating in some of them. Depending on the electrical and magnetic properties of the material, their interaction with microwaves can be classified as one of three types [5]:
Material/microwave interaction representation classified according to their behavior: (a) transparent, (b) opaque, and (c) absorbent.
A fourth type of interaction known as mixed absorption has also been proposed. In this particular case, mixed or multi-phase materials with different degrees of microwave absorption are sought after. Most electrically insulating ceramics such as alumina, MgO, silica, and glasses are transparent to microwaves at room temperature, but, when heated above a certain critical temperature
In order to explain the interaction of absorbing materials with microwave radiation and the energy transfer that occurs during this interaction, several physical mechanisms have been proposed. These mechanisms include bipolar rotation, resistive heating, electromagnetic heating, and dielectric heating. Depending on the material, the response to incoming radiation can be attributed to one mechanism or a combination of several of them:
Finally, the fourth mechanism,
The degree of interaction between the microwave electric and magnetic field components with the dielectric or magnetic material determines the rate at which energy is dissipated in the material by the various mechanisms. The properties of the material that are most important for the interaction are the permittivity
When microwaves penetrate the material, the electromagnetic field induces motion in the free and bound charges (electrons and ions) and in dipoles. The induced motion is resisted because it causes a departure from the natural equilibrium of the system, and this resistance due to frictional, elastic, and inertial forces leads to the dissipation of energy. As a result, the electric field associated with microwave radiation is attenuated, and heating of the material occurs.
\nThe dielectric interaction between materials and microwave radiation can be described by two main parameters [6, 28, 29, 30]:
absorbed power, P
depth of microwave penetration, D
Both parameters play a critical role in the uniform heating of the material. The absorbed power is the volumetric absorption of microwave energy (in W/m3) and is expressed according to the following equation:
\nwhere
The loss tangent,
where
The loss factor,
Relationship between factor loss and absorbed power at a frequency of 2.45 GHz and room temperature for some common materials.
Both,
A general explanation is based on a fundamental body, such as a grain particle, in its neutral state containing polarized molecules distributed in random positions. These molecules can easily be reoriented by the effect of an external electric field, as shown in Figure 4.
\nPosition of the molecules (a) in its natural state, and (b) with the application of an external electric field.
If the polarity of the electric field is changing constantly, molecules will modify their orientation accordingly in a very fast manner so as to align with the field (Figure 5) and, as a consequence, heat will be generated due to the friction among them and electrical resistive effects from unbound charges. The material heats up as a function of the absorbed energy during this process.
\nRepresentation of the reorientation of the molecules in the presence of an alternating electric field, such as that induced by microwaves.
The main difference with respect to conventional sintering is the direction of heat flow [31], because in conventional sintering, heat is transferred from the surface of the material toward the inside due to the heating mechanisms involved. In contrast, in microwave sintering, in the presence of a strong electric field, molecules vibrate with the same intensity and at the same time generate heat throughout the whole material as a consequence of the characteristics of dielectric heating.
\nThe second main parameter in microwave/material interaction is microwave penetration depth,
High frequencies in combination with high dielectric property values translate into superficial heating of the material, while low frequencies with small dielectric property values give place to volumetric heating.
\nBased on the properties of materials, it is well known that those with a high conductivity and permeability present a lower penetration depth for a given frequency. The penetration depth of many materials oscillates around 1 μm, which means that heating tends to stay at the surface. If powders with a particle size of approximately that of
A microwave oven is composed of three main elements: (1) microwave source, which is in charge of generating the electromagnetic radiation, (2) transmission lines, which transmit the microwaves, and (3) a resonant cavity, which is where the interaction with matter takes place [28].
\nThe theoretical principle that governs each of the components is based on Maxwell Equations [30, 32]:
\nwhere
Maxwell equations are the physical laws that describe an electromagnetic field and its variations with time. The design of an efficient microwave system to process materials requires understanding of electromagnetic theory.
\nIn the following paragraphs, a description of the different components that are part of a microwave system is given.
\nMagnetron schematic showing all the elements required for generation of microwave radiation.
The open space between the plate and the cathode is referred to as interaction space. In this space, electric and magnetic fields interact to exert a force on the electrons. Given that an electric charge creates an electromagnetic field around it, all the electrons, moving in circles in the cavities, produce electromagnetic waves, in this case microwaves, perpendicular to their own displacement and with a frequency that depends on the size of the cavities.
\nUsually, for microwave heating applications, the frequency of the generated electromagnetic radiation is 2.45 GHz. This frequency corresponds to one of the so-called Industrial, Scientific and Medical (ISM) frequencies, which are free of utilization for these types of applications. The insertion of magnetrons in commercial microwave ovens for home use has translated in more economical sources of this frequency by allowing the fabrication of magnetrons in a large scale. Moreover, other ISM frequencies are also employed for heating applications, such as Bluetooth and WiFi [33]. The power generated by the magnetron can be controlled by changing the amplitude of the cathode’s current or the intensity of the magnetic field.
\nThe size of a single-mode resonant cavity must be in the order of one wavelength. Additionally, in order to maintain a resonant mode, these systems require a microwave source that allows frequency variations or that the cavity dynamically changes its size to couple the frequency of the microwaves. Generally, the distribution of the electromagnetic field in this type of cavity is well known. With an adequate cavity design, the microwave field may be focalized to a particular zone where the material sample can be sintered. An additional advantage for this type of cavity is the fact that the dielectric properties of the material can be monitored during sintering.
\nMulti-mode cavities are able to maintain several modes simultaneously. The design of home microwave ovens is based on this type of cavity. The greater the size of the cavity, the higher the number of possible resonant modes. Hence, multi-mode cavities are larger than a wavelength, which contrast with the size of single-mode systems.
\nThe presence of different resonant modes results in the existence of multiple hotspots inside the cavity. Local fluctuations in the electromagnetic field can result in overheating of certain areas. In order to minimize these hotspots, the electromagnetic field must be uniform. Field uniformity can be achieved by increasing the size of the cavity and varying the sample position dynamically, for example, with a rotating plate or stirrers. By increasing cavity size, the number of modes increases and, as a consequence, the heating patterns of each mode begin to superimpose and the stirrers or the plates change the distribution of the field inside the cavity.
\nOne of the main issues associated with microwave sintering of materials is their initial microwave radiation absorption and heating. Most of the processing is carried out at a relatively low frequency of 2.45 GHz, which makes the initial heating of the material very difficult to control. Another important problem that may arise consists in the thermal instability that materials are prone to due to the changes in their properties, such as their dielectric constant,
Temperature gradients that arise during heating can produce microcracking and an unequal distribution of resulting physical properties, such as density and hardness. Therefore, thermal insulators or coatings may be necessary to avoid the presence of these gradients. Nonetheless, these insulators can provoke the control loss of the temperature.
\nCeramics tend to exhibit an abrupt increase in
A plausible solution that materials scientists and engineers have developed consists on a hybrid method that combines direct microwave heating coupled with heat transfer coming from another material that surrounds the specimen to be sintered [37]. This system is an example of mixed absorption heating, with a high dielectric loss at both low and high temperatures.
\nIn this scenario, microwaves are absorbed by the material with highest dielectric losses at room temperature while microwaves propagate through the material with lower losses at room temperature. Heat and energy are transferred from the absorbing material to the transparent material. This type of heating makes use of a specific component known as a susceptor. This heating-aid element is the absorbent material and possesses a very high dielectric loss at room temperature, transmitting heat to the material to be sintered via conventional heat transfer mechanisms. Once the material has heated sufficiently surpassing its
This combined action, known as microwave hybrid heating, can be employed for fast sintering of compacted powders. In this particular case, the direction of heat flow in the specimen to be sintered occurs in two directions: from the surface to the nucleus due to the effect of the susceptor and from the nucleus to the surface once it is able to absorb microwave radiation [37]. A representation of a bidirectional hybrid heating can be seen in Figure 7.
\nSequence diagram of microwave hybrid heating for material sintering: (a) before exposure to microwave radiation, (b) susceptor heating under MW radiation, and (c) specimen to be sintered able to absorb MW energy giving place to bidirectional hybrid heating.
Mechanical properties and microstructure of Y-TZP-sintered materials are strongly influenced by the degree of densification and grain nucleation that result due to the sintering process. This is, in turn, determined by the heating mechanisms that take place within the material. Current commercial sintering of ceramic materials is based on conventional heat transfer mechanisms: conduction, convection, and radiation. In this case, heat is generated from heating elements and a temperature gradient arises, as heat is transferred from the surface to the material’s core. This method, however, requires long processing times. As a consequence, grain broadening occurs [38], which leads to a decrease in the final mechanical properties of the material [39]. It also requires a high-energy consumption to reach such high temperatures, which must also be maintained for long periods of time (around 2–4 h or more) if fully dense materials are desired.
\nOne advantageous and useful non-conventional method that can modify the densification mechanisms and results in faster processing of Y-TZP ceramics is microwave sintering [40]. The energy conversion of electromagnetic radiation into heat by the material itself due to the material’s dielectric properties is the driving force for densification [41]. The rise in temperature is determined by the amount of energy absorbed in the process. The acceleration of diffusion mechanisms during sintering by the oscillating electric field has also been proposed by some authors to explain enhancement of the sintering process, in what is called a “microwave effect” [42]. Because it is a non-contact technique, the effects of differential sintering are minimized [43], which is another advantage over conventional sintering methods, where differential densification is an important problem that arises from the slow heating rates.
\nThe dielectric loss factor of zirconia is quite different from those of other oxide and non-oxide ceramics. At a frequency of 2.45 GHz, zirconia does not couple adequately with microwaves at room temperature. The loss factor, ε”, of Y-TZP at room temperature is similar to microwave-transparent materials, with a value of approximately 0.04 [25]. However, the dielectric loss increases tremendously with temperature, reaching a value of almost 100 at 1000°C. Therefore, zirconia can become a very absorptive material by raising its temperature. In order to achieve this, two different approaches can be found:
With the aid of a susceptor, generally (SiC), as it has been described in the previous section. This method is the most commonly found in the study [25, 44, 45].
Employing conventional resistive elements to initially heat the zirconia until its Tc is reached, and zirconia is able to interact with the microwave field by itself [46].
Previous reports [4, 18, 47] have demonstrated that with microwave sintering, highly dense materials can be obtained without a substantial grain coarsening because dwell time is considerably shorter and heating rates are quite high in comparison with conventional sintering [48]. Energy consumption is also significantly reduced as a consequence of the mechanisms involved in microwave heating and the abovementioned shortening of processing times. As a result, several advantages arise including improved mechanical properties and reduced environmental impact [5, 49]. This method may provide lower costs for professionals and customers maintaining or even improving the quality of the final product.
\nIn general, the study suggests that microwave sintering of zirconia can result in comparable mechanical properties and high degrees of densification comparable to those achieved with conventional sintering systems at lower dwell temperatures and significantly shorter sintering times [50, 51, 52, 53, 54]. Moreover, some studies have demonstrated that microwave-sintered specimens exhibit enhanced crystallinity [55] and improved mechanical properties [18, 49, 56].
\nOver the past few decades, the lithium aluminosilicate (LAS) compositions have been extensively studied because it is very low or even negative thermal expansion compounds have found a wide application field including cookware, bakeware, electronic devices, telescope mirror blanks, ring-laser gyroscopes, and optically stable platforms [57]. Sintered negative thermal expansion materials have usually low mechanical strength because the expansion anisotropy causes microcracking. This is due to different extents of thermal expansion in different crystallographic orientations, which induces internal stress with temperature change. On the other hand, it has been reported by Pelletant et al. [58] that the microcracking depends on the grain size; therefore, an increase in the β-eucryptite grain size causes a progressive microcracking and consequently a more negative bulk of thermal expansion coefficient. Nevertheless, the usefulness of these thermal properties in the production of materials with null expansion has a wide range of potential engineering, photonic, electronic, and structural applications [59].
\nβ-Eucryptite is the most negative thermal expansion phase in the lithium aluminosilicate system, and therefore β-eucryptite has been thoroughly studied [60]. Compared with the number of studies of glass–ceramic materials, there are few studies in the literature, which deal with this system as a ceramic material in the solid state [61]. This is important because as far as possible, obtaining 100% theoretically dense materials in this system in solid state would improve the mechanical properties as such modulus of elasticity compared with glass-ceramic materials with similar thermal shock characteristics. In LAS system, the high temperatures required to fully densify ceramic powders result in large grain sizes due to Ostwald ripening when traditional sintering techniques are used [38]. This makes obtaining dense materials with nanometric and submicrometric grain sizes extremely difficult, and, as a consequence, the sintered materials do not achieve high mechanical properties. To overcome the problem of grain growth, non-conventional sintering methods have emerged as promising techniques [62, 63, 64, 65].
\nSpark plasma sintering (SPS) was reported in [62] as a non-conventional sintering technique for LAS materials that can lead to high relative dense ceramics with no or with very low amounts of a glassy phase. This technique is restricted to materials with disk forms of different diameters, whereas materials with a near-net-shape approach have still not been possible to obtain. Moreover, Vanmeensel et al. [66] reported that the temperature distribution inside the tool and specimen is not homogeneous during the spark plasma sintering technique, especially, for electrical insulating samples (such as LAS ceramics), due to temperature gradient existing between the border and the center of the sample in the intermediate and final stage of sintering. Other important factor to consider is the high-energy consumption of SPS technique.
\nMicrowave heating is a non-conventional sintering technique to solve the difficulties found with previous techniques such as SPS. The microwave technique was specially designed to fabricate ceramic LAS bodies with a high density, a very low glass proportion, and high mechanical properties (hardness and Young’s modulus) [63]. An important characteristic associated to microwave process, it is possible to directly obtain materials with complex parts (
Previous reports [63, 64, 65] confirmed the possibility of successfully obtaining well-densified β-eucryptite ceramics by using microwave sintering technology with glass-free at relatively low temperatures (1200°C) and very low energy consumed (<80 W). Figure 8 shows the temperature profile and microwave-absorbed power during the sintering process of an LAS specimen [63]. The figure shows a microwave experiment with a resident time of the ceramic sample of 10 min around 1200°C. The LAS material is a good absorber of microwave radiation at 2.45 GHz, and this implies that the heating is homogeneously distributed throughout the material. The dilatometric data presented for the cryogenic temperature interval are essential in order to design these kinds of materials for space applications in which controlled and very low thermal expansion behavior are needed at very low temperatures. This is the case of mirror blanks in satellites, where exceptional thermal properties are demanded together with exceptional mechanical properties, that is, the β-eucryptite sample sintered at 1200°C shows Young’s modulus of 110 MPa and a hardness of 7.1 GPa values [63]. Compared with other heating modes, conventional, and spark plasma sintering [64], the most important characteristics associated to microwave process are the rapid and volumetric heating, which improves the final properties of the materials.
\nTemperature profile and microwave absorbed power during the sintering process of the LAS specimen.
During sintering process, the heating occurs by the three conventional heat transfer mechanisms: conduction, convection, and radiation. Conduction results by heat diffusion between surfaces in contact, for example, in walls inside the furnace that are in contact with the compact. Convective heat transfer occurs from the bulk flow of the gas in the furnace to the compact surface. Thermal radiation is emitted by high-temperature furnace elements and converted into electromagnetic energy that is transferred to the surroundings. The compact receives this electromagnetic energy causing it to heat up. Heat from radiation is, however, quite low, and most of the heating of the compact occurs by means of conduction and convection. Due to the nature of heat transfer mechanisms involved in this method, the surface of the material always heats first, and a temperature gradient between the compact surface and the interior of the material arises, resulting in heat flow from the surface to the bulk. As a consequence, considerably long dwell times (>2 h) are required in order to obtain a complete temperature homogenization and uniform heat distribution.
\nAnother important sintering approach is pressure-assisted sintering, which consists in the external application of pressure during the heating process. Four main ways can be employed to apply pressure. The first one is hot pressing (HP), resulting from uniaxially applying pressure to the powder in a die. The second one is sinter forging, which is similar to hot pressing but without confining the sample in a die. The third one is called hot isostatic pressing (HIP), which consists in the isostatic application of pressure by means of a gas. The fourth one is spark plasma sintering (SPS) and flash sintering which is similar to HP but using a high heating rate. Pressure-assisted sintering enhances the rate of densification significantly relative to the coarsening rate [27]. However, an important disadvantage of pressure-assisted sintering is the high cost of production being only available for specific industrial applications that require specialized, high-cost components. Another limitation is that only simple shapes can be processed due to the use of dyes.
\nCurrently, most commercial materials are processed by conventional sintering and SPS. One of the major drawbacks of these systems, particularly for ceramics, is the high-energy consumption required to reach such high temperatures and dwell times in order to obtain an adequate densification and mechanical properties. Therefore, new approaches on sintering of these materials need to be explored. For example, employing furnaces for heating components with small dimensions would not be energetically efficient. Hence, sintering systems with a focalized energy delivery to the material, such as microwave sintering, can decrease energy use significantly. Moreover, techniques must be flexible and allow for the processing of near-net-shape materials because complex and unique pieces are needed since shapes vary completely from one application to the next. Therefore, microwave sintering confirms as an interesting alternative for the processing of advance ceramics.
\nCurrently, innovative sintering methods are being explored and studied in order to reduce energy consumption and production costs, as well as processing tools that allow modification of the densification mechanisms that may improve the microstructure and mechanical properties of sintered materials. The main purpose for modifying sintering mechanisms is to obtain relative densities close to theoretical values, while maintaining a controlled, but limited, grain growth. Potential of microwaves in material processing has been identified several decades ago. However, owing to limited understanding of the phenomena, their use remained largely confined to only a few materials. Moreover, the overwhelming success of microwave in communication overshadowed its application in other areas. However, discrete attempts in material processing yielded many breakthroughs. In the last 65 years, the microwave processing of materials has become popular due to its potential advantages over the conventional techniques. Overall, microwave sintering is a very good alternative for sintering and consolidating commercial materials for structural applications due to the resulting finer microstructure, enhanced mechanical properties, and reduction in processing times and energy consumption.
\nThe authors gratefully acknowledge the funding support of the Spanish Ministry of Economy and Competitiveness (JCI-2011-10498, IJCI-2014-19839, and RYC-2016-20915), the Generalitat Valenciana (GV/2014/009, GRISOLIA/2013/035, and GRISOLIAP/2018/168), Universitat Politècnica de València, and Dr. A. Presenda and Dr. R. Benavente for contributing to the research work described in this chapter.
\nThe first decade of the twenty-first century belongs to a new wireless world indeed! The rapid growth of cellphones, Wireless Local Area Networks (WLANs), and recently the wireless Internet, in short, wireless communication is driving the whole world toward greater integrity with wireless communications. By 2020, two-thirds i.e. 66% of total IP traffic shall be occupied by Wi-Fi and mobile devices whereas wired devices will account for 34% of IP traffic in access network [1]. Licensed bands claim to be heavily congested but different research work shows that the channels in the form of time and frequency are still available. In future, wireless networks may face the problem to find suitable frequency spectrum to fulfill the demands of future services. To solve the problem of inefficient use of spectrum utilization, a new concept is evolved known as cognitive radio (CR) [2]. In 1999, Joseph Mitola III introduced the concept of CR. This new concept of CR which is called as intelligent wireless communications is capable of sensing its environment and dynamically accessing the technology. It adjusts according to the input variations of statistical data for: (a) very dependable communication wherever and whenever needed; and (b) efficiently utilizing the radio spectrum [3, 4]. This can be done by sensing the radio environment: (i) by finding spectrum bands which are unused by the PU (i.e., licensed user), and (ii) by allocating unused bands of radio spectrum to SU requesting service [5]. The underused frequency bands of PUs are called as in-band spectrum holes [6]. The spectrum holes can be used to allocate the channels to CR user. However, to ensure efficient communication for such unlicensed communication, the Quality of Service (QoS) parameters need to be considered. Quality of Service can be defined as a set of specific requirements provided by a network of users, which are necessary in order to achieve the required functionality of a service.
Cognitive radio (CR) concept is based on vacant spectrum in licensed band which sometimes referred to as combination of channels. In telecommunication, a channel refers either to a physical transmission medium such as wire or to a logical connection over a multiplexed medium such as a radio channel. Global System for Mobile Communication-900 (GSM-900) has been allocated an operational frequency from 890 to 960 MHz. GSM uses the frequency band 890–915 MHz for uplink (reverse) transmission, and for downlink (forward) transmission, it uses the frequency band 935–960 MHz. The available 25 MHz spectrum with 100 kHz guard band at two edges of the spectrum is divided into 124 Frequency Division Multiplexing (FDM) channels, each occupying 200 kHz as mentioned in Figure 1.
Frequency channels in GSM-900.
A large amount of information is transmitted between the MS and the BS, particularly, user information (voice or data) and control or signaling data. Depending on the type of information transmitted, different logical channels are used. These logical channels are mapped onto the physical channels (time slots). In the GSM system, a traffic channel will be made by a combination of a 200 kHz frequency channel and one of the eight time slots. For example, digital speech is carried by the logical channel called the traffic channel which during transmission can be allocated to a certain physical channel. There are two basic types of logical channels in GSM: traffic channels (TCHs) and control channels (CCHs). TCHs are used to carry either encoded speech or user data both in the uplink (UL) and downlink (DL) directions. The CCHs are used to communicate service between network equipment nodes.
Code Division Multiple Access (CDMA or Interim Standard-95) uses the frequency band 824–849 MHz for uplink (reverse) transmission, and for downlink (forward) transmission, it uses the frequency band 869–894 MHz. With CDMA, all users share the same 1.25 MHz wide carrier, but unique digital codes are used to differentiate subscribers. The codes are shared by both the mobile station and the base station and are called “pseudo-random code sequences”. Base stations in the system distinguish themselves from each other by transmitting different portions of the code at a given time. In other words, the base stations transmit time-offset versions of the same pseudo-random code.
The 3rd Generation Partnership Project (3GPP) and 3rd Generation Partnership Project 2 (3GPP2) have indicated that orthogonal frequency division multiple access (OFDMA) is the choice for the physical-layer transmission technology in 4G standards. In OFDM, usable bandwidth is divided into a large number of smaller bandwidths that are mathematically orthogonal using fast Fourier transforms (FFTs). Reconstruction of the band is performed by the inverse fast Fourier transform (IFFT).
CR utilizes both licensed and unlicensed bands for communication. Among these bands, GSM bands have less attenuation; their wavelength is more resilient to phenomenon like diffraction, absorption, scattering, etc. GSM channels use FDM-TDM technique with low bandwidth of 200 kHz and hence better scalable. In practice, technologies like CDMA, OFDM, etc. uses large bandwidth and total allotted spectrum and hence, only chance to obtain large bandwidth is the un-allotted part of the licensed band. Cognitive radio users (human and machine) are low end users (users in lower or lowest economic bracket or free public utility users with minimum vocabulary or information) and expected mainly to use voice, short message and short data services. These reasons make GSM is a good choice for cognitive radio implementation.
The QoS for mobile services which has been defined by ITU-T includes different parameters of QoS like availability, accessibility, maintainability and user perception of service. These parameters have been defined in context of cognitive radio in Section 2. Availability refers to detection of unused spectrum by way of signal strength measurements. In conventional method, the signal strength of a received radio signal is measured. The measurement setup used for detection of spectrum holes in CR along with the cognitive radio issues for availability has been discussed in detail in Section 4. The proposed work calculates blocking probabilities both on immediate minute occupancy basis and its preceding 60 min basis at the instant of service request by SU. The new concept of channelized blocking probability has been defined along with the general definitions of blocking probabilities in Section 5. An algorithm has been developed to accept SU service requests with different classified Quality of Service (QoS) from a set of PU channels. Allocation of a PU vacant channel on SU call request is done based on prediction that the channel will remain vacant for more than the assessed holding time of SU. The channel allocation model works based on inputs from (a) the channel call arrival rate prediction model and (b) SU holding time assessment model and has been discussed in Section 6. The model accepts collected data as input in time serial manner for running through residual lifetime based prediction model program. The comparison of proposed work has also been done and its results and conclusion has been discussed in Section 6.
Quality of Service (QoS) is the capability of a network to offer better service to selected network traffic over specific underlying technologies [7, 8]. The various parameters for QoS are:
Availability: The operator maintains a dynamic list of available channels. When the user wants to communicate, operator is liable to assign one or more communication channel to the user as per his demand and within tolerable specified time limit. In case of telecom service, this delay is maximum 6 s but usually, the delay noticed is less than a second. This function is referred to as availability.
Accessibility: When the operator assigns channels to the user, the user equipment (UE) should be capable to use the allocated spectrum to the extent possible. For example, when a 200 kHz channel is allocated for some time τ, the user handset should be able to communicate at highest modulation supported by operator and RF condition. This phenomenon is called accessibility. Proper handshaking shall take place between UE and access network (AN) before establishing communication at acceptable speed by both ends.
Maintainability: In mobile communication, as the user is mobile, there is a continuous change in environment and RF condition. The operator has to take into consideration various parameters like speed of communication, handover, etc. for proper maintenance of established communication. This is known as maintainability.
User perception of service: It is the ability to deliver the service meeting the user’s quality of expectations. It is measured by the customer satisfaction using access equipment behavior audit, drive test for mobile as pseudo customer and actual satisfaction through interrogation by customer survey specialists.
Over the last few years, a lot of research has undergone on spectrum sensing (SS) techniques for the detection of spectrum holes [9]. Energy detection (ED) approach, also known as radiometry or periodogram, is a popular technique for spectrum sensing due to low computational and implementation complexities [10]. The conventional SS method includes waveform-based sensing (WBS), matched filter-based sensing (MFBS) and cyclostationary-based sensing (CBS). WBS is a coherent method that correlates the received signal with the previous patterns available in database [11]. This technique is susceptible to synchronization errors which can cause false detection of primary users [10]. MFBS is the best detecting method where the received signal is interrelated with the transmitted signal [12]. The periodic characteristics of the received signals i.e., pilot sequences, carrier tones, etc. is explored by CBS technique [13]. It requires less time to achieve high processing gain due to coherent detection. In MFBS technique, it is assumed that it has the previous information of the primary’s signal. It indicates that method is not suitable in some bands as some of the communication technologies are not operating with the previous information. On the other hand, CBS is unfeasible for signals that don’t show cyclostationarity properties. CBS has high computational complexity [14]. Energy-based sensing (EBS) is the easiest SS method [15, 16]. This technique does not require any previous knowledge of primary user’s signal but its performance is less when noise’s variance is unknown or at the higher side [17]. Energy-based sensing based on sub-Nyquist sampling shall be beneficial as per as sensing duration is concerned [18]. The performance of the EBS is characterized where the PUs reflects a constant characteristic during the sensing period as well as during the sensing period where PUs can alter their ON/OFF status, thus, affecting the spectrum sensing decision [19]. A brief comparison various SS techniques is enlisted in Table 1 as follows [10, 20]:
In mobile communication, primary user occupied channels are known to network. So, a new call is eligible to occupy any of the vacant channels. In contrast, in cognitive radio network, a dynamic spectrum management is used which shall include information about the traffic pattern of the channels occupied by primary users at an instant. Basically, a CR should characterize whether the traffic pattern is static or dynamic and based on that it should use different methods for idle time prediction before selecting a channel.
Much of the spectrum below 50 GHz is available for low-powered unlicensed use. Based on environmental variations, the utilization of the licensed band is approximately 15–85% [21]. The actual utilization of mobile communication spectrum in licensed band has not yet been taken into consideration. The variation of channel utilization for various types of cities has also not been studied. These studies may be very useful to perfectly recognize the frequency channels with no active or low occupancy so that the CR technology can be successfully deployed. Few such studies has been mentioned below:
A spectrum measurement campaign for a frequency band of 75 MHz to 3GHz was conducted through a survey in an outdoor urban environment for a continuous period of 48 h at Barcelona, Spain [22]. The six consecutive frequency bands of 500 MHz were formulated and it was found that only 22.57% of the whole frequency range was utilized.
To find the spectrum occupancy for the frequency range from 80 MHz to 5.85 GHz, another survey was conducted at Institute for Infocomm Research’s building in Singapore for 24-h over 12 weekday periods [23]. It was observed that the average utilization of frequency band was only 4.54%.
At the Loring Commerce Centre, a similar survey was conducted during a normal work week for 72 h for the frequency band of 100 MHz to 3 GHz [24]. In the survey, it was found that only 17% of the average spectrum is utilized during the measurement period. The ISM bands and mobile licensed bands are partly utilized and the remaining part of the spectrum band resembles noise.
In India, the RFs are being used for different types of services like mobile communication, broadcasting, radio navigation, satellite communication, defense communication, etc. The wireless equipment are developed and manufactured based on the spectrum utilization in the country as decided by the National Frequency Allocation Plan (NFAP). The various frequency spectrums allotted to mobile communication services is shown in Table 2 [25, 26].
Comparison of different spectrum sensing methods.
Licensed spectrum of various wireless technologies.
* As per available information.
CR technology has been developed to dynamically access and release channels in licensed bands. There is a scope of getting the unutilized channels in licensed spectrum with or without having a stable infrastructure for CR. Thus it is expected that at zero cost public authorities providing public utility services may be authorized to operate over unutilized spectrum even though licensed. However, such public utility service providers are very limited. Field test should be essentially conducted for the evaluation of quasi-permanently unused channels for use of in-band common control signaling purposes.
To dynamically measure the occupancy rate of the PUs and to calculate the quantum of vacant channels available for CR use, a measurement setup called drive test equipment is used that collects data on a moving vehicle. A motor vehicle containing mobile radio network air interface measurement equipment is used in the drive test. The equipment measures different types of virtual and physical parameters of mobile cellular service in a given geographical region. Data relating to the network itself is collected by drive test equipment, radio frequency scanner information, services running on the network such as voice or data services and GPS information to provide location logging. The hardware and software used in the setup includes data cable and global positioning system (GPS), digital radio frequency (RF) scanner, laptop with charger and USB hub license dongle for TEMS, engineering handsets with 4 (2G/3G) SIMs of different operators mounted simultaneously and cable terminal, cell site database and link budget, clutter diagram from Google website, MapInfo software. In the setup, data collection software is installed in the laptop where mobile set is used along with GPS. Data related to signal strength, downlink and uplink frequency etc. is collected by the mobile whereas GPS collects the data of latitude and longitude of each point. All the information is stored with its geographical locations along with their respective time and date.
Data was collected for spectrum utilization measurements in GSM 900 MHz band in an outdoor environment of other cities viz. Bhopal, Ranchi, Patna, Dibrugarh, Shillong & Port Blair with population in the range of 1.5 million to 6.6 million as per 2011 census. The study reveals that there is 74.19% spectrum occupancy in lower band in Bhopal, while in Ranchi it is only 52.42% as it switches to upper band where it has spectrum occupancy of 83%. In the lower band of Patna, the measurements indicate that there is 75.8% spectrum occupancy. Shillong, the capital of Meghalaya state of India is located at 25.57°N and 91.88°E on a plateau in the eastern part of the state. The population of the city is 1.43 million where spectrum occupancy is 54%. Port Blair located at 11° 40’ N and 92° 46′ E is the capital city of Andaman and Nicobar Islands in India. The next survey was conducted at Port Blair which is the municipal council in the southern part of Andaman, a part of India’s Union Territory. Being the lowest populated area, it has spectrum occupancy of 43%. It is evident that most of the bands in lower band of various cities are quasi-permanently vacant. These vacant channels can be used for control signaling in CR communication.
The spectrum occupancy of eight cities of India is represented in Figure 2. It is shown in the diagram that there is 30% occupancy for the most sub-urban area with less population. For population between 1 million to 4 million, the increase is almost linear. In the range of population between 4 million to 7 million, it is observed that the occupancy reaches a saturation level. Also, with projected expansion of highly populated city core areas to 8 million, occupancy level is projected to reach up to 86%, leaving a clear space of 14% of channels for CR use [25].
Population spectrum graph.
As population increases, there will be requirement of more number of channels and this need can be managed through effective optimization methods. This can be mathematically calculated as negative requirement of channels and graphically expressed as saturation. Due to continuous growth in population, operators can request access for TCHs from higher frequency band and consequently, the occupancy at lower frequency band is reduced. Thus, the channel occupancy with growth in population can empirically be given as:
where x = size of population in millions, y = channel occupancy percentage in lower frequency band.
Thus, it is found that 20% or more of the licensed bandwidth is almost practically unused even in a saturated market environment. In other words, more than 1/8th part of all bands were not in use which closely matches the need of one signaling channel for 7 traffic channels. This is more than the channel demand of 12% of the whole band to get access to the whole of the bandwidth at a time by cognitive radio and is adequate to take additional MAC level overhead required for CR. There is no urgent requirement of these channels by the licensed operators, whereby it can be carefully allotted as common control channel for CR purpose. Further, to doubly enhance protection of common control channel, a disaster recovery common control channel may be designated. It will hold the replica of allotments and processing status of CR Primary Common Control Channel. The above findings for common control channel are highly dynamic and sensed information may be able to provide user mobility in a most competitive environment at an economically affordable cost. The approximation of channel occupancy is possible depending upon the population and hence CR technology planners can propose a long term plan for efficient use of it for public benefits.
As per as the need of QoS is concerned, CR networks should have the ability to choose the best frequency band for use [27]. Spectrum decision is based on the channel characteristics and operations of PUs. Spectrum decision follows two steps: (i) every spectrum band is distinguished based on the statistical information of PUs and the local observations of CR users [28]. The available spectrum holes represent different characteristics that differ over time. (ii) After the available spectrum bands are characterized by considering spectrum characteristics and the QoS requirements, the most appropriate spectrum band should be selected. To minimize capacity variation, spectrum decision method is used which incorporates minimum variance-based spectrum decision (MVSD) scheme. To maximize the total network capacity, a maximum capacity-based spectrum decision (MCSD) scheme is used [29]. Accordingly a database is maintained where the data is purified based on signal to noise ratio (SNR), vacant holding time, etc. which is then used for channel allocation to SU.
After the holes are detected and best selected in licensed band, the next function of the CR user includes accessing the channel which is known as spectrum sharing. The wireless channel needs the synchronization of transmission attempts between CR users. The spectrum sharing aims to address four aspects:
As per the architecture, the classification can be distributed or centralized. In centralized spectrum sharing, a central entity controls the procedures of spectrum allocation and access. In distributed spectrum sharing, local or probably global policies that are independently executed by each node decide the spectrum allocation and access [30].
Based on allocation behavior, spectrum access can be cooperative or non-cooperative. In cooperative (or collaborative) allocation, the interference measurements of each node is exploited in such a way that it considers the effect of the communication of one node on other nodes. In non-cooperative allocation, only a single node is considered. To promote cooperation among conflicting decision makers, efficient spectrum sharing schemes such as game theory have been used for more efficient, flexible, and fair spectrum usage [31, 32, 33, 34].
Based on the access technology, it is of two types: overlay and underlay. In overlay spectrum sharing, nodes access the network using the spectrum band that has not been used by PUs so as to minimize interference to the primary network. In underlay spectrum sharing, the spread spectrum techniques are exploited such that the transmission of a CR node is regarded as noise by PUs [35].
Spectrum sharing methods are concentrated on two types of solution: where spectrum sharing can be within a CR network, which is called intranetwork spectrum sharing; and among multiple coexisting CR networks, which is called internetwork spectrum sharing.
The conventional telecom uses hourly prediction for estimating mobile communication traffic. In hourly prediction, peak time is not determined neither in the beginning of the hour nor at the end of hour. The clock hour is not authentic as it does not tell exactly about the peak time at which the traffic was maximum. Hourly traffic data is insufficient to decide whether a call can be initiated at a particular instant of time. Thus, it becomes necessary to study minutewise traffic pattern. Minutewise occupancy data is computed on hourly basis for various cells of varying channel numbers, e.g., each cell with 7/14/28/60 channels for 1/2/4/8 radio frequencies (RFs) of GSM system to assess the traffic behavior of PU. There are channels with same number of RFs which are lightly loaded at some places as well as highly loaded at some other locations.
Conventionally, a telecom operator analyzes the total traffic on hourly basis and identifies the busy hour where total traffic is maximum. Hourly data arranged on weekly basis does not give a clear picture of the peak hour as it contains many peaks. The traffic variations within a clock hour are not predictable in weekly analysis. Thus, data arranged on daily basis is taken to estimate the behavior of hourly traffic. For example, the minutewise collected occupancy data was taken on hourly basis for 50 cells of different channel numbers. Figure 3 depicts daily occupancy pattern for the locations with 60 channels (8 RFs) and differently loaded at various traffic places [36, 37]. The results are similar for other RF counts also. The figure indicates double heaps in the channel occupancy. The heap pattern shows near parabolic nature from 07:00 to 15:00 and 17:00 to 22:00 h.
Daily channel occupancy pattern for three cells with 60 channels.
Usually, peak traffic is bell-shaped around peak few minutes. Hence, the busy hour may or may not include peak traffic minute which is of serious concern for prediction of channel availability. The clock hour is not authentic as it does not exactly tell about the peak time at which the traffic was maximum. The PU busy hour is redefined in context of CR as 1 h during which peak channel occupancy occurs and calculates the growth and decay of traffic 30 min each around peak traffic minutes.
The determination of QoS provided by a particular network configuration is required for an efficient design of communication networks. The Grade of Service (GoS) is a benchmark used to define the desired performance of a particular cellular communication system by specifying a desired probability of a mobile subscriber obtaining channel access given a specific number of channels available in the system. The concept of trunking allows a large number of mobile subscribers to share the relatively small number of available channels in a cell by providing access to each mobile subscriber, on demand, from a pool of available channels.
Cellular communication systems are examples of trunked radio systems in which each mobile subscriber is allocated a channel on a per-call request basis. Upon termination of the call, the previously occupied channel is immediately returned to the pool of available channels. When a mobile subscriber requests service and in case all of the radio channels are already busy, the incoming subscriber call is blocked, or denied access to the system. In some communication systems, a queue may be used to hold the requesting mobile subscribers until a channel becomes available.
The GoS is a measure of the ability of a mobile subscriber to access a cellular system during the busiest hour. The busy hour is based upon the subscriber’s demand for the service from the system at the busiest hour during a week, month or year. It is necessary to estimate the maximum required capacity in terms of available channels and to allocate the proper number of channels in order to meet the GoS. GoS is typically specified as the probability that a call is blocked, or the probability of a call experiencing a delay greater than the predefined queuing time. A call which cannot be completed at the time of call request made by a mobile subscriber is referred to as a blocked call or lost call. This may happen due to channel congestion or non-availability of a free channel. In other words, GoS is a measure of channel congestion which is specified as the probability of a call being blocked, or the probability of a call being delayed beyond a specified time.
When the offered traffic exceeds the maximum capacity of the system in terms of the allocated number of channels, the carried traffic becomes limited due to the limited number of channels. The maximum traffic is the total number of channels in Erlangs. Let us consider a cellular system that is designed for a GoS of 2% blocking. This implies that the channel allocations for cell sites are designed in such a way so that 2 out of 100 calls requested by mobile subscribers will be blocked due to channel congestion during the busiest hour.
Practically, there are two types of trunked cellular systems. The first type of trunked cellular system offers no queuing for call requests, which is known as “Erlang B” system. This means that for every mobile subscriber making a service request; it is assumed that there is no set up time to a requesting mobile subscriber. He mobile subscriber is given immediate access to a channel if it is available. If no channels are available, the requesting mobile subscriber is blocked without access to the system and is free to try again later. This type of trunking is called “blocked calls cleared or blocked calls lost”. It assumes that calls arrive as determined by a Poisson distribution.
In performance evaluation of cellular systems or telephone networks, Erlang B formula is a formula for estimating the call blocking probability for a cell (or a sector, if sectoring is used) which has N “trunked” channels and the amount of (“offered”) traffic is A Erlang [38]:
where, i = 1 to N denotes the steady-state number of busy servers. It is directly used to determine the probability B that call requests will be blocked by the system because all channels are currently used.
The second type of trunked cellular system is called “blocked calls delayed” and its measure of GoS is defined as the probability that a call is blocked after waiting a specific length of time in a queue. In this system, a queue is provided to hold the calls requested which are blocked. If a channel is not available immediately, the call request may be delayed until a channel becomes available. Customers who find all N servers busy join a queue and wait as long as necessary to receive service. The probability of a call not having immediate access to a channel is determined by the “Erlang C” formula [38]. If no channels are immediately available, the call is delayed. The Erlang C formula is expressed in terms of blocking probability as:
where, N = number of trunks or service channels, A = offered load.
The basic unit of channel busy/idle status is recorded for each frequency and each time slot. All the channel activities (busy/idle) during each time slot and frequency correction are monitored through different counters. In addition, user friendly graphical user interface (GUI) is available from where the data can be collected and stored in backup support. Conventionally, the data related occupancy of channel is collected on hourly basis from the counters like m15, m16, m25, m17, m18, m23, m147, and m148. Telecom occupancy related data is stored in several counters of base station controller (BSC). To get secondwise accurate data, an interrupt driven learning mechanism is required which is practically not used in telecom network because the requirement is purely academic. In earlier telecommunication, very few processors were used in the radio access logic boards. The speed of working of processors was much less as compared to present day. Further, minutewise transfer of counter data to a central computer adds to transport overhead and hence avoided. Presently, the data speed is available in processors along with high speed links. Hence, capturing of minutewise data is now feasible. Thus, the counters are read every minute for free/occupied status. A scale below minutes was not explored due to the reason that the measurement traffic is a great appreciable part of the total signaling traffic. Thus, disastrous situation cannot be introduced in a live system.
The data for individual subscriber was taken offline from Billing Center at extreme leisure hour for few subscribers. It was expected that a similar set of users shall be the SUs also. For example, the minutewise occupancy of 32 channels during a busy hour has been taken into consideration and is shown in Figure 4 which helps to determine the availability of spectrum holes. The red color cell indicates that the channel is busy or in dedicated mode and cannot be used for channel allocation to CR. The green color cell indicates that the channel is free and can be used for CR use after ensuring its QoS parameters. The parameters like call arrival rate and user holding time of PUs is predicted for the purpose of utilization of channel by SUs.
Minutewise occupancy chart for 32 channels in a day during busy hour.
Blocking probability can be estimated by channel occupancy during last clock hour, e.g., 9 am–10 am at 10 am, 10 am–11 am at 11 am, etc., as in classical teletraffic theory and this estimation has been further improved through prediction models. In present chapter, clock has been considered only for hourly prediction purpose. For channel allocation, considering the instant of channel request as origin, an observation hour is defined in 2 more ways viz., (a) each hour has been composed of 60 immediately preceding minutes or channelized minutes, (b) current minute, or instantaneous minute.
For a lost call system, the GoS for CR shall be measured by using modified Poisson’s model, as proposed in this chapter is given by the equation:
where, k = 0 to (c−1) with c = total number of trunked channels, N = Np + Ns, Np = count of PUs in the system, Ns = σNp + offset = count of SUs in the system, where, 0 < σ ≤1. A portion of the PU, σ (known as SU factor) can be considered for the calculation of the blocking probability of a secondary call combined with PUs traffic in the system. Also, 0 < offset <1 such that Ns is an integer of higher value. These values of GoS help to determine whether the channel allocation to SU shall be successful or fail.
Consider a network with ‘n’ licensed channels (j = 1 to n) where the wireless nodes are static. A CRN is located within the licensed coverage area of licensed operator. The CRNs are equipped with spectrum sensor devices. The sensors monitor and report channel states to the central node via dedicated channels. Also, the outcome of the sensor state can be represented by binary signal {0,1}, where ‘0’ represents the vacant state and ‘1’ represents the occupied state of observed channels at an instant of time, t. All the channels are sensed assuming that the sensing time is very less than the duration of idle and busy time. The history database is periodically updated with the new sensing information. The collected database of different channels can be used to compute the different blocking probabilities as described below to estimate GoS.
The probability computed by autoregressive moving average (ARMA) model that is a mathematical model of the persistence, or autocorrelation, in a time series is called as PBP. In ARMA model, a time series is observed for total number of calls (y1,y2,….yT). To predict the total number of calls in dth day, forecast is done by minimizing the mean squared error (MSE), i.e., Min.y’ T + d E = ((yT + k – y′T + d)2). In that case, the best forecast is the mean of yT + d, conditional on the information up to T, (y1,y2,….yT):
The BS monitoring system records the minutewise channel occupancy of licensed users for continuously 7 days of a week. The predicted value of offered load during the 8th day is calculated by using data of total calls of a particular hour for 7 days (i.e., T = 1 to 7) using ARMA model and has been depicted in Table 3 [39]. The predicted value of total calls of 8th day of a particular hour is taken for computation of blocking probability using the formula:
where i = 0 to c = total channels in the system.
Prediction of offered load in a particular hour using ARMA model.
The blocking probability provided by the system at an instant of time, (t + 1), is called as IBP. The IBP is on every minute basis as shown in Table 4 [39]. In this case, the offered load,
where, i = 0 to c = total channels in the system.
Calculation of offered load at an instant of time t = (t + 1). (a snapshot taken from software).
The blocking probability provided by the system at an instant of time (t + 1) considering the traffic of the preceding 60 min is called as CBP and is depicted in Table 5 [39]. The offered load in this case is defined as,
Calculation of offered load based on immediate preceding 60 min data.
where, i = 0 to c = 60×n = total channels in the system.
The values of CBP and IBP helps to decide the probability of success whenever a SU initiates a request. The data has been chosen at peak busy hours for 50 channels and minutewise occupancy for 300 min calls is practically taken for estimation purpose for various trunk servers ranging from 7 to 50 channels. The CBP as shown in Table 5 can be computed by the program developed by the author.
Figure 5 is plotted for comparison of CBP and PBP for consecutive 4 h. It is evident that the standard deviation of PBP is fixed with respect to IBP but the standard deviation of CBP matches with that of IBP during the busy hour which shows that the CBP is better than PBP [39]. The CBP is much more prominent during the peak hours where random variation of instantaneous values is more.
IBP, CBP and PBP vs. time in minutes in the system with trunk servers (c) = 22.
The error is estimated by the computation of standard deviation between IBP and PBP, and IBP and CBP. The standard deviation of the sample is the degree to which individual data within the sample differ from the sample mean. Since PBP is fixed for a clock hour, the error between IBP and PBP is given by:
where x = value of IBP, x
As CBP varies minutewise, the error between IBP and CBP is given by:
where, xi
It is evident from Table 6 [39] that as the number of trunk server increases, error between IBP and CBP {calculated using equation (Eq.(10))} is less than that of error between IBP and PBP {calculated using equation (Eq.(9))}. Thus, the estimation of CBP is a better method than the estimation of PBP. The eligible list of channels available for use by SU can be formed where the channels have blocking probability ≤0.02.
Difference between standard deviation of IBP & CBP vs. IBP & PBP.
Whenever a SU initiates a call, at an instant, the blocking probability P from Eq. (4) is measured at that instant for all channels in the cell. The value of blocking probability must be less than some pre-determined value. An observation for different channels was made with c = 29, 44, 60 servers to assess the blocking probability and is shown in Figure 6 [37].
Call blocking probability with trunk servers (c) = 29 using Poisson’s model.
It is observed from Figure 6 that when the primary channel occupancy <50% then the CR-BS is capable of providing mobile channel to the SU with blocking probability less than 0.02 which is equivalent to wireline. The channels which have blocking probability less than 50% are eligible used for allocation to SUs.
After capturing the best available spectrum by CR, the user may change its operating frequency band(s) that may require modifications to the operation parameters, based on the PU activity. This process is referred to as spectrum mobility. The purpose of the spectrum mobility management in CRN is to ensure smooth and fast transition that may lead to minimum performance degradation during a spectrum handoff.
CR users and CR infrastructure are essentially the identical as licensed authorized user system. But CR systems shall follow the guideline that: (a) only free channels of PU are to be used and when PU is active, the channels shall be returned to PU immediately, (b) it will not create any noise to PU system. Thus, architecture of SU should have some extra logic than PU system, otherwise they are similar. Therefore, there is a necessity to understand complete system architecture of PU along with PU traffic behavior for making conclusions about CR traffic handling effectively. This can be done by using prediction models.
The parameters like call arrival rate and user holding time of PUs can be predicted for the purpose of utilization of channel by SUs. The probability that the channel would be accessible for a given time period is evaluated according to the prediction or estimation results. The evaluated probability is then compared with some threshold, according to which, SUs can decide whether to use this channel or not. For the purpose of prediction, the study has been arranged in two broad divisions viz. (a) daily traffic analysis for long term prediction; (b) minutewise traffic analysis for immediate prediction of availability of vacant channels.
Primary channel is allotted by the network operator according to demand. The channel occupancy is recorded during each hour. The traffic pattern of each channel is seasonal in terms of daily traffic. The present study uses long term prediction model to compute call arrival rate of the PU. It takes the weekly values of call arrival rate during a particular hour as an input and predicts its weekly values for the same hour. These predicted values can be used to assess the hourly traffic of PU, based on which SU channel allocation is done.
In SARIMA, weekly data of each cell was gathered and organized on hourly basis and one particular hour was selected and analyzed. SARIMA model was used to forecast call arrival rate of weekly data for a specific hour depending on monthly monitored data of the same hour. The prediction of traffic pattern for a week follows the following relationship:
where, i = 1 to 7 for 1 week is assumed as a seasonal unit, j = 1 to n, n = count of days for observation, Si = seasonal coefficients; and,
where, A0 & A1 = intercept coefficients obtained from SARIMA modeling.
A study of channel occupancy pattern shows that the occupancy varies every hour in a day and again daily occupancy pattern has variations over the days of a week. SARIMA uses moving average and auto regression methods which assures sample variations from predicted channel occupancy rate
where, α, β and γ are the smoothing parameters and usually their values are chosen heuristically; at is the smoothed level at time t, bt is the change in the trend at time t, st is the seasonal smooth at time t, p is the number of periods per season.
Here, term j is omitted from λ(j,t), and is written as λt for simplicity. The Holt-Winters algorithm requires starting (or initializing) values given by equations as below:
The HW forecasts are then calculated using the latest estimates given by the equations (Eq. (13)), (Eq. (14)) and (Eq.(15) that have been applied to the series. Thus, the predicted value for
where su is the smoothed estimate of the appropriate seasonal component at u, bu is the smoothed estimate of the change in the trend value at time u and au is the smoothed estimate of the level at time u.
The minutewise occupancy data is used as compared to hourly occupancy data that has been used in traditional traffic prediction models.
The conventional telecom uses hourly prediction for estimating mobile communication traffic. In hourly prediction, peak time is not determined neither in the beginning of the hour nor at the end of hour. The clock hour is not authentic as it does not tell exactly about the peak time at which the traffic was maximum. Hourly traffic data is insufficient to decide whether a call can be initiated at a particular instant of time. Thus, it becomes necessary to study minutewise traffic pattern. In the present work, minutewise occupancy data was computed on hourly basis for various cells of varying channel numbers.
The nature of traffic distribution for few cells at busy hours around the peak for half an hour on both the sides with time resolution of 1 min is studied. For prediction of channel occupancy by PU, it has been established that: “The rate of change of occupancy at a particular point of time near peak time is proportional to its separation from peak time” [43]. Mathematically, it can be expressed as:
where, y = occupancy of primary channels at time t, tp = expected time where peak occupancy occurs, m
which is the equation of a parabola with: h = −b/2a, n = ah2 + bh + c; (h, n) are the equation of the vertex. h = tp.
The peak of parabola may be different from peak occupancy minute. Also peak occupancy projected at peak of parabola shall be different from actual peak obtained.
The authors Hao Chen and L. Trajkovic had captured 92 days (2208 h) of traffic data to study calling behavior of users [44]. They concluded that (a) time scale of minutes is too small for recording the calling activity as an average holding time of a call is usually 3–5 min, (b) time scale larger than an hour (day) is too coarse to capture. The other authors Xiukui Li and Seyed A. (Reza) Zekavat have used the concept of accessing the channel for SU which is vacant with maximum duration [45].
Hence, most of the computations in telecommunication industry are based on hourly number of calls. Accordingly, existing literatures have indicated the need for counting free lifetime of a channel as a probabilistic parameter based on hourly occupied time and hence unaware about residual lifetime, particularly in case of ON/OFF traffic channel conditions.
Methods like dynamic spectrum access (DSA) are proposed to access the channel but the actual channel allocation is not taken into consideration. In case of CR, spectrum or channel mobility is the main challenge as the SU can only access a call without interfering the PU. The program in the present work shall determine the best channel eligible for allocation to SU using the concept of mean residual lifetime (MRL). The procedure for computation of MRL has been described below.
The PU channel state shall be considered as {Ho, H1} where, Ho = free and H1 = occupied. The primary status as sensed or predicted by the SU is shown in Figure 7 [JAF1]. False alarm and missed detection occurs during assessment which leads either to an inefficient system or interference with PU.
Channel occupancy in binary state.
Considering a small unit of time ‘τ’ which is the minimum time period such that BS can upload scanned RF data to Mobile Switching Centre (MSC) and fusion center without affecting routine CR operation. ‘T’ is the time period during which the traffic is recorded based on pulled data from different CR-BS counters and used for statistical records e.g. number of seizures of a channel per hour, total holding time of the channel per hour etc. A SU can request for a channel anytime within τ. The request is conveyed to the fusion center where the decision for allocation of a suitable channel is taken based on MRL and particular requesting SU’s channel holding time profile.
T will be taken as an hour and (1/λ) in minutes. It is also considered that ‘τ’ is in minutes. It is also further considered that:
τ is the atomic unit of time and further decomposition of it is not practically feasible,
MSC is updated by BS every τ units of time,
MSC updates warehouse every T units of time
MSC updates SU traffic data in warehouse every T units of time
Channel occupancy request (PU&SU) is instantly passed on by the BS to MSC in real time t
These aspects will be taken up for application in different models.
Let λ is the number of calls arrived on a particular traffic data acquisition interval T. If th is the call holding time of a SU requesting a free PU channel at any time t, then the probability that none of the PU occupies a channel till t = t + th is given by:
where,
Let F be the lifetime distribution with discrete random sample and no call arrival intervals Γ1, Γ2, ……, Γn in the span of observation T. We arrange them in order such that:
The empirical mean residual lifetime (MRL) is defined as:
and, mn(t) = 0 for te ≥ Γnn and k = 0,1,2,….,(n-1).
where, mn(te)|j = mean residual lifetime of jth channel which has ‘n’ number of vacant intervals at observation instant ‘t’ which can be offered a SU call.
Here, te is the time which has elapsed since it became free; Γj = mean residual lifetime of jth channel at an instant t of SU call offer, and j = 1,2,…., r with r = total number of vacant channels at an instant ‘t’ of offer.
The program developed by the author computes probability of success using the method proposed by Li and Zekavat, i.e., without MRL and with MRL. Figure 8 depicts the probability of success with and without MRL for various trunk servers vs. time demanded by CR. The Figure 8 clearly depicts that the proposed model using MRL method is superior than the method used by previous researchers [46].
Comparison of probability of success with and without MRL vs. time demanded by CR for trunk servers = 43 and 50.
A traffic model is required to represent traffic characteristics and to estimate the performance evaluation of the volume of traffic load place on network capacity and subscriber mobility. The present work assumes that the arrival rate is Poisson’s distributed. The inter-arrival rate is also assumed to be exponentially distributed. The traffic model is determined assuming a certain number of channels in a cell system. The two crucial factors for mobile communication in the traffic pattern are called arrival rate of the channel and user call holding time. Hence, the traffic model is developed based on the prediction of call arrival rate and the study of holding time distribution of individual customers.
A telecommunication network consists of expensive hardware (trunks, switches, etc.) which carries telecommunications traffic (phone calls, data packets, etc.). The physical network is fixed, but the traffic is random for which it is designed, i.e., the call arrival rate and the user holding time is unpredictable. Thus, to accommodate this random demand of traffic, the network designers must predict the call arrival rate for allocation of resources. The usual assumption in classical teletraffic theory is that the call arrivals follow a Poisson’s process. The Poisson’s assumption is consistent with data for voice traffic when the calls are generated by a large number of independently acting subscribers.
The French Mathematician Simeon Denis Poisson developed Poisson’s formula. It states that for non-overlapping events, arriving at an average rate λ, the probability of ‘s’ arrivals in time t equals:
Poisson’s distribution is taken for traffic measurement as it is based on memoryless system and it generally gives a better estimate of the traffic related parameters.
Telecom occupancy related data is stored in several counters of Base Station Controller (BSC). To get secondwise accurate data, an interrupt driven learning mechanism is required which is practically not used in telecom network because the requirement is purely academic. In earlier telecommunication, very few processors were used in the radio access logic boards. The speed of working of processors was much less as compared to present day. Further, minutewise transfer of counter data to a central computer adds to transport overhead and hence avoided. Presently, the data speed is available in processors along with high speed links. Hence, capturing of minutewise data is now feasible.
The channel occupancy duration depends on individual person depending upon profession, status, time of day, etc. and varies widely at different hours of a day. This is most predominant in case of speech communication when the channel holding time depends upon caller and various customers called parties. Thus, the holding time data shows different variation at different levels of the time series and hence, a transformation of the data series can be useful. The Box- Cox method is used to transform this data into normality. The Box-Cox method obtains a normal distribution of the transformed data (after transformation) and a constant variance.
Let us denote original observations hi,u,p as hp for the ith user at uth hour and write the series as h1,h2,---,ht and transform the observations as w1,w2,---,wt. According to Box-Cox principle [47]:
where, ζ is a parameter used to compute the confidence level (CL). Using the values of ζ = {−2,−1,−0.5, 0, 0.5, 1, 2} and to gain confidence level (CL) limit up to 95%, the optimum value of ζ shall be used to assess hp as:
where, hp = optimal holding (service) time of the user at time t.
The Box-Cox method can be used to decide the optimum holding time of the user.
On placement of service request by an SU, all vacant channels from eligible list has to be evaluated for allocation based on (i) predicted call arrival rate during the hour created through long term table; (ii) IBP at the instant for primary decision on allocation; (iii) mean residual lifetime of the channel at the time of service request; (iv) expected service holding time of a particular user requesting service; (v) CBP at the instant based on short term table. Finally, the best channel with highest probability of survival is selected for offer to the incoming SU traffic [46].
The interworking of different blocks for channel allocation is shown in Figure 9 and is described below:
Channel traffic updation: The Gateways (GWs) monitor PU activities using dedicated RF scanners. G1,G2,…,Gk are responsible for monitoring PU activities as well as for SUs. Any change in channel occupancy is passed on by GW to CR-BS and MSC in real time t. BS maintains several counters for traffic recording purposes. The counts of the counters are polled by MSC every ‘τ’ interval and then the counters are reset. When T = kτ where, k = 2, 3,… MSC prepares a table for the call arrival rate for T interval for each channel and deposit to the warehouse where the data is stored in format λ(j, u) where, j = channel number and u = current T period number. This module also provides idle time information d1, d2,…, etc. in τ units since a channel is free.
SU traffic updation: SU traffic information is recorded in the billing register after the completion of each call. The traffic details in respect of each SU are stored in warehouse. It is used for predicting holding time of SU at the time of service request.
Channel traffic prediction: Primary channel is allotted by network operator according to demand. The channel occupancy is recorded during each T. The predicted value for channel occupancy
As soon as a new predicted value of call arrival rate is available during a particular hour, HW updates its estimated three components (level, trend, seasonal) for that particular hour. The value of smoothing constant for each component falls between zero and one. Larger smoothing constants mean more weight is placed on the value suggested by the new predicted value and less on the previous estimate. This means that the method will adapt more quickly to genuine changes in the call arrival pattern.
The list of channels {C1, C2,…} which satisfies the above condition are predicted during the last hour and are then transferred to the channel allocation model for assessment of holding time during the current hour of allocation.
SU holding time assessment: The holding time data shows different variation at different levels of the time series and hence their transformation is done by Box-Cox method using (Eq.(25)) and the optimum holding time needed by SU is obtained by (Eq.(26)).
Channel allocation model: The set of eligible channels obtained by (Eq.(22)) are with residual lifetime:
Channel selection block diagram.
where, hp = service time needed by SU at time t.
The eligible channel set {c1, c2,…, cr} is arranged and the probability of the success in the offered jth channel shall be:
A sequential flowchart of data flow from all the blocks and computation of MRL has been given in Figure 10. Based on the measurement of the call arrival rate, holding time of PUs and various parameters of QoS, the channel allocation is done.
Process for channel allocation.
Minutewise traffic data acquisitioned online is collected in a table in OMC-R. SU can raise service request at any time. To serve channel allocation engine inputs,
At the beginning of each hour, for all channels predicted call arrival rate
estimated holding time in minutes for the service request from SU holding time assessment model;
all channel occupancy status is ‘free’ or ‘busy’ mode for last 60 min starting from current minute from channel traffic updation module is taken, and
their MRL is computed.
Finally, channels with highest probability of survival given by Eq. (28) are selected for offer to the incoming SU traffic.
A trial run for 25 times each for available channels 7, 15, 22, 29, 36, 43 and 50 were carried for holding times from 1 to 14 min in the program developed by the author. A snapshot of the program is depicted in Figure 11, where different SUs demand varying holding times. The ‘red’ color indicates that the channel is busy while the ‘green’ color indicates that the channel is free. Whenever an SU requests a channel at time t for a certain holding time, the channel is allocated to that particular SU if the channel is free for continuous holding time demanded by SU, and is shown in ‘yellow’ color in Figure 11. If the channel is busy even for the last holding time demanded by SU, the program indicates that the channel allocation is unsuccessful and is shown with ‘red’ color in continuation with yellow color.
A snapshot of the program for channel allocation at an instant of time t.
To study success rate for channel allocation various cases were considered with varying available number of channels and different holding times demanded by SU, where the total number of channels in the cell is denoted by tch.
Case (i): Consider tch = 15 and the time demanded by the SU = 2 min. The result is obtained when the program is initialized at 100th min and stopped at 102nd min out of 300 min total available data. At 102nd min, when a call request is made by SU, the total available vacant channels are 5 out of 15 channels. The values of blocking probabilities are obtained as IBP = 0.03649 and CBP = 0.0308. The MRL is computed for all the five vacant channels. The MRL of the five channels are 4.7, 0.82, 0.5, −27 and −27.25. As one of the channels has maximum MRL and has value > holding time demanded (2 min), it is selected for channel allocation to SU. The result is depicted in snapshot given in Figure 12 and is shown with continuity of yellow color for 2 min, which signifies that the channel allocation was successful. Table 7 shows analysis of success rate for 15 channels with various holding times demanded by CR.
Validation result for total channels = 15; SU holding time demanded = 2 min.
Computation of success rate for total channels = 15 for CR holding time = 1 to 14 at various runs of the program.
Case (ii): Consider tch = 43 and the time demanded by the SU = 9 min. The result is obtained when the program is initialized at 80th min and stopped at 82nd min out of 300 min total available data. At 82nd min, when a call request is made by SU, the total vacant channels are 14 out of 43 channels. The values of blocking probabilities are obtained as IBP = 1.102×10−3 and CBP = 6.442×10−4. The MRL is computed for all the 14 vacant channels. The MRL of the 14 channels are: 4, 0.75, 0.444444, −0.6875, −1.5, −2.6, −4, −5.66667, −5.8, −11.6667, −12.5, −13.5714, −13.8, −14.2222, −20. As the MRL of one of the channels has value > holding time demanded (9 min); thus, the channel allocation was successful. The result is depicted in snapshot given in Figure 13 and is shown with yellow color which signifies that the channel allocation was successful. Table 8 shows analysis of success rate for 43 channels with various holding times demanded by CR.
Validation result for total channels = 43; SU holding time demanded = 9 min.
Computation of success rate for total channels = 43 for CR holding time = 1 to 14 at various runs of the program.
Thus, the channel allocation is successful as the count of channels increases in the system with the precondition that the MRL > time demanded by the SU.
The data was chosen at busy hours for various channels ranging from 15 to 50 and minutewise occupancy for 300 min calls is taken for simulation purpose. A program has been developed for real time offering and verification if the call request succeeds or fails for the duration demanded by SU. The program calculates the predicted λ upto last hour at background and has been included in simulation program as offline. Similarly, holding time needed for SU has also been imprinted interactively. The program accepts any number of PU channels upto 50 selectively at the time of trial. The position of occupancy can be seen on screen starting from any instant after 60 min upto 300 min for any duration. Also, one or more SU calls can be offered to the system and minute by minute observation of SU call progress can be monitored on screen.
The program was run repetitively and at random, under various channel availability conditions and differently demanded holding time. The result has been plotted in Figure 14, where the probability of success (or QoS) has been calculated as:
where, number of trials were 25 times for each run condition at random input time.
Probability of success rate vs. time demanded by CR for various trunk servers.
As depicted in Figure 14, in case, if the threshold probability in licensed band is taken to be 0.8, the success rate for CR users is still achieved if the channels are ≥36 and holding time ≤2.5 min demanded by the SU [46]. When the threshold probability for SU is 0.5, the success rate is achieved if the channels are ≥22 with minimum holding time ≤ 2.5 min demanded by the SU. Thus, when the PU channel occupancy is 50%, the CR-BS shall provide mobile channel to SU with blocking probability ≤0.02, where industry standard for blocking is 2% (0.02).
Thus, the CR-BS shall be capable of providing vacant channels to SU. Grade of Service (GoS) of SU is at par with GoS standard specified for PU when traffic intensity is below 50%. Success rate of channel allocation is increased as number of channels increases in the system.
In mobile communication network, despite heavy usage of communication channels, vacant channels are available which can be used for cognitive radio network. From the present work it is found that practically 20% or more of the total licensed bandwidth is permanently unused or vacant even in a crowded region This is well above the need of 1 out of 8th part of the band to get access to the whole of the bandwidth at a time by CR and adequate to take additional MAC level overhead required for CR. Remaining 80% of the licensed bands are dynamically vacant which can be used for traffic purpose. Based on results, an empirical relationship has been established for channel occupancy for a city, where such survey has not been conducted but population is known. The blocking probability of the channels are available for allocation and duration of remaining unoccupied can be mapped to the instant status of the channels. The present work has established that when the PU channel occupancy is 50%, the CR-BS shall provide voice channel to SU with blocking probability ≤0.02, where industry standard for blocking is 0.02. The GoS improves linearly with total number of channels in the system at a given per channel availability. It is also evident that Erlang theory is effective for Poisson’s distribution theorem with ≥20 channels for GoS to achieve.
A program that has been developed to accept SU service requests with different QoS from a set of PU channels can be used for dynamic allocation to SU. A SU call request can be placed in such a dynamic environment and status of the SU call progress can be noticed till the end of requested holding time. The mean residual lifetime (MRL) of the free channels was computed based on requesting SU call holding time for PU channel allocation to a requesting SU. The channel with highest MRL is allocated. If the threshold probability in licensed band is taken to be 0.8, the success rate for CR users is achieved if the channels are ≥36 and holding time ≤ 2.5 min demanded by the SU. When the threshold probability for SU is 0.5, the success rate is achieved if the channels are ≥22 with minimum holding time ≤ 2.5 min demanded by the SU. Thus, success rate of channel allocation increases with the increase in number of channels in the system.
The proposed model shall be deployed to provide services for IoT through wireless communication. The implementation of the proposed work shall be a good solution where high volume of devices with low mobility is required for new wireless technologies.
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