Feature similarities of images restored from various super-resolution methods.
\r\n\tSolar radiation is the radiant energy that originated from the sun in the form of electromagnetic radiation at various wavelengths. Solar radiation is the source of renewable energy and can be captured and converted into various forms of energy (e.g. electricity and heat) using different technologies.
\r\n\tA very vast amount of solar energy reaches the atmosphere and surface of the earth and solar energy has been used for heating purposes for a very long-time and after solar cells’ invention in 1954, solar cells have also been used widely for electricity generation. Solar cells convert the sunlight into electricity by the creation of voltage and electric current through the so-called photovoltaic effect.
\r\n\tPhotovoltaic (PV) solar energy has attracted significant attention in the recent decade as a reliable source for power generation due to various merits such as the free source of energy, abundant materials resources, environmentally friendly and noise-free, longtime service life, requiring low maintenance, technological advancements, market potential, and very importantly, low cost. The growth of using photovoltaic (PV) solar energy as a promising renewable energy technology, is being increased more and more worldwide. Therefore, much further research is needed for possible future developments in the field of solar photovoltaic energy.
\r\n\tThe aim of this book is to provide detailed information about solar radiation as the source of photovoltaic (PV) solar energy for a broad range of readership including undergraduate and postgraduate students, young or experienced researchers and engineers.
\r\n\tThis should be accomplished by addressing the various technical and practical aspects of solar radiation fundamentals, modeling and the measurement for photovoltaic (PV) solar energy applications.
\r\n\tThe majority of this book should describe the basic, modern, and contemporary knowledge and technology of extraterrestrial and terrestrial solar irradiance for photovoltaic (PV) solar energy.
\r\n\tThe book covers the most recent developments, innovation and applications concerning the following topics:
\r\n\t• Fundamental of solar radiation and photovoltaic solar energy
\r\n\t• Solar radiation and photovoltaic solar energy potential
\r\n\t• Solar irradiance measurement: techniques, instrumentation and uncertainty analysis
\r\n\t• Solar radiation modeling for photovoltaic solar energy applications
\r\n\t• Solar monitoring and data quality assessment
\r\n\t• Solar resource assessment and photovoltaic system performance
\r\n\t• Solar energy and photovoltaic power forecasting
\r\n\tThese are accompanied with other useful research topics and material.
",isbn:"978-1-83968-859-1",printIsbn:"978-1-83968-858-4",pdfIsbn:"978-1-83968-860-7",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"4c3d1319d7286e81bfb15c1f4b20460a",bookSignature:"Dr. Mohammadreza Aghaei",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9862.jpg",keywords:"Solar Radiation Modeling, Solar Data Assessment, Solar Monitoring, Solar Radiation Forecasting, Solar Irradiance Measurements, Solar Instruments, Solar Spectral Distributions, Uncertainty Analysis, Solar Cell Technologies, Photovoltaics (PV), Solar Resource Assessment, Photovoltaics Power Forecasting",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 17th 2020",dateEndSecondStepPublish:"October 15th 2020",dateEndThirdStepPublish:"December 14th 2020",dateEndFourthStepPublish:"March 4th 2021",dateEndFifthStepPublish:"May 3rd 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"A senior researcher in the field of photovoltaic solar energy, a postdoctoral scientist at Eindhoven University of Technology (TU/e), Chair of the WG2: reliability and durability of PV in EU COST PEARL PV.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"317230",title:"Dr.",name:"Mohammadreza",middleName:null,surname:"Aghaei",slug:"mohammadreza-aghaei",fullName:"Mohammadreza Aghaei",profilePictureURL:"https://mts.intechopen.com/storage/users/317230/images/system/317230.jpg",biography:"Mohammadreza Aghaei is a senior researcher in the field of photovoltaic solar energy, Eindhoven University of Technology (TU/e), The Netherlands. He is chair of the Working Group 2: reliability and durability of PV in European Cooperation in Science and Technology, COST Action PEARL PV.\nHe received the M.S. degree in electrical engineering from the Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia, in 2013, and the Ph.D. degree in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2016.\nHe was a Postdoctoral Scientist with Fraunhofer ISE and Helmholtz-Zentrum Berlin (HZB)-PVcomB, Germany, in 2017 and 2018, respectively. He is a Guest Scientist with the Department of Microsystems Engineering (IMTEK), Solar Energy Engineering, University of Freiburg since 2017. He is currently a Postdoctoral Scientist with the Design of Sustainable Energy Systems Group, Eindhoven University of Technology (TU/e), The Netherlands. He has authored numerous publications in international refereed journals, book chapters, and conference proceedings. The main his research interests include Solar Energy, Photovoltaic systems, PV monitoring, LSC PV, solar cells, machine learning, and UAVs.\nDr. Aghaei is a member of the International Energy Agency, PVPS program-Task 13 and International Solar Energy Society, and also an MC member in EU COST Action PEARL PV.",institutionString:"Eindhoven University of Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Eindhoven University of Technology",institutionURL:null,country:{name:"Netherlands"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"10",title:"Earth and Planetary Sciences",slug:"earth-and-planetary-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247865",firstName:"Jasna",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247865/images/7225_n.jpg",email:"jasna.b@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. 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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:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"57185",title:"Diffusion-Steered Super-Resolution Image Reconstruction",doi:"10.5772/intechopen.71024",slug:"diffusion-steered-super-resolution-image-reconstruction",body:'Before deepening into the super-resolution imaging, let us discuss the term resolution. Most people, particularly those not in the imaging field, define resolution broadly as the physical size of an image. For a two-dimensional digital image, this definition implies an area in the image given as the product of the number of pixels in the horizontal and vertical dimensions (pixel or picture element is the smallest unit of information in a digital image). In this context, therefore, a high-resolution image contains a higher pixel count than a low-resolution image. Figure 1(a) includes features with higher perceptual qualities than those in Figure 1(b), but both images have equal sizes. From the figure, therefore, we see that dimension only seems inadequate to define the resolution of an image.
Images of dimensions 2179 × 2011.
Resolution, more generally, means the quality of a scene (image or video). Five major types of image resolutions are known: pixel resolution, spectral resolution, temporal resolution, radiometric resolution, and spatial resolution. The use of these variations depends on the application. Pixel resolution refers to the total number of pixels a digital image contains. Hence, both images in Figure 1(a) and (b) possess equal pixel resolutions of 2179 × 2011. In other words, each image is approximately 4.4 megapixels (2179 × 2011 = 4,381,969 pixels ≈4.4 megapixels). Unfortunately, pixel count offers fraction of the pieces of information contained in the image. For a colored image with red, green, and blue channels, an individual pixel can only accommodate the details of a single color. Spectral resolution describes the ability of an imaging device to distinguish the frequency (or wavelength) components of an electromagnetic spectrum. Imagine spectral resolution as the degree in which you can uniquely discern two different colors or light sources. Temporal resolution refers to the rate at which an imaging device revisits the same location to acquire data. When dealing with videos, for example, the term implies an average time between consecutive video frames: a standard video camera can record 30 frames per second, implying that every 33 ms, this camera captures an image. In remote sensing, temporal time is usually measured in days to represent time that a satellite sensor revisits a specific location to collect data. Radiometric resolution defines the degree at which an imaging system can represent or distinguish intensity variations on the sensor. Expressed in number of bits (or number of levels), radiometric resolution provides the actual content of information in the image. Spatial resolution explains how an imaging modality can distinguish two objects. In practical situations, spatial resolution describes clarity of an image and defines the resolving power of an image-capturing device. The perceptual quality of an image increases with the spatial resolution. This research presents super-resolution imaging as one of the available techniques to enhance the spatial resolution of an image.
Most people are naturally inclined to high-quality and visually appealing images that contain adequate details. However, this demand is not always achieved because of some imperfections in the imaging process. Therefore, scholars have proposed hardware and software approaches to address the challenge. The former approach requires sensor modification, and it may be achieved by reducing the physical sizes of the pixels—a process that increases pixel density (number of pixels per unit area) on the surface of the sensor [1]. The hardware approach gives perfect resolution enhancement, but the technique endures several drawbacks: (1) it introduces shot noise into the captured images, (2) it makes the imaging device costly and unnecessarily bulkier, and (3) it lowers the charge transfer rate because of the increased chip size [2]. These challenges have prompted scholars to search for software techniques, which are cost-effective and reliable, to improve the spatial resolution of an image without effecting circuitry of the imaging device. In this case, an image can be captured by a low-cost device and processed to generate its corresponding high-quality version.
The classical software approach that has gained a considerable attention of scholars is called super-resolution [3–6], which uses signal processing principles to restore high-resolution images from at least one low-resolution image. Super-resolution techniques can be put into two major categories: single-frame-based, which generates a high-resolution image from the respective single low-resolution image [7, 8], and multi-frame-based, which exploits information from a sequence of degraded images to generate a high-quality image [2, 6]. The current work builds on the multi-frame super-resolution framework, which implicitly encourages noise reduction from the input low-resolution images. The framework bridges total variation (TV) [9] and Perona and Malik [10] smoothing functionals and allows for these functionals to interact in such a way that super-resolution and preservation of critical image features are simultaneously conducted.
The multi-frame super-resolution framework can better be understood through a conceptual degradation model, which shows how an unknown high-resolution image, u, undergoes a variety of degradations to form M low-quality images, yk, with k = 1, …, M denoting positions of the low-resolution frames (Figure 2). In practice, the degradation process of u to generate yk involves warping, blurring, decimation (downsampling), and noising, respectively defined in this work by the operators Wk, Bk, Dk, and ηk: warping introduces rotations and translations into u, hence changing its geometrical properties; blurring reduces sharpness of features in u; decimation samples u and lowers its physical size; and noising corrupts u with noise, assumed to be additive.
Image degradation model.
Figure 2 can be transformed into
which explains how the degradation model generates frame k in a set of low-resolution images. The goal of the present study is to estimate u under the degradation conditions, and one approach to achieve the goal is to re-define Eq. (1) into the minimization problem that aims to lower ηk. Therefore, using the Lp norm, where p ∈ [1, 2] (the range 0 ≤ p < 1 is excluded because the values of p contained in this interval lead to nonconvex minimization problems that are susceptible to unstable solutions), the formulation to optimize u becomes
where E is modeled as an energy functional that defines noise level in the degraded image. The gradient of the cost of E in Eq. (2) is
where
For p = 1, Eq. (4) evaluates to
which shows that, after shifting and zero filling,
Downsampling matrix, D, and upsampling matrix, DT, applied on an image. The resolution reconstruction factor used is two for both horizontal and vertical dimensions of the image.
For p = 2, Eq. (4) becomes a solution of the L2 norm minimization, or
which was proved in [12] that it represents pixel-wise mean of measurements. The L2 norm is less-robust against erroneous data, but the metric has better mathematical properties: convexity, differentiability, and stability. Therefore, several scholars prefer the L2 objective functions in situations where data contain low noise as in our case.
The super-resolution problem, whether formulated through L1 or L2 norm, has an ill-posedness nature. Given that r is the resolution factor, then for the under-determined case, or for M < r2, and for the square case, or for M = r2, the problem may evaluate to infinitely many undesirable solutions. Also, for the small amount of noise in the data, ill-posed problems tend to introduce larger perturbations in the final solutions. These issues can be effectively addressed through a technique called regularization, which has another advantage of speeding the convergence rate of the evolving solution. This work addresses the super-resolution ill-posedness through regularization functionals from nonlinear diffusion processes, which have been reported that they can preserve important image features (edges, contours, and lines) [13–15]. The proposed regularizer integrates total variation (TV) [9] and Perona and Malik (PM) [10] models that complement one another to generate appealing results.
Considering the super-resolution ill-posedness property, a hybrid framework combining TV and PM regularization kernels has been formulated. The framework includes additional parameters, α and β, which establish a proper balance between TV and PM during regularization. The objective is to de-emphasize weaknesses of the models and amplify their strengths so that the super-resolved images are superior.
In [9], Rudin et al. established the TV model that explains how noise in the image can be reduced. The model is based on the fact that a noisy image contains a higher total variation, defined by the integral of the absolute gradient of the image or
where ρ is the TV energy functional, Ω defines the domain under which u exists, and x denotes the two-dimensional spatial coordinate on Ω. Therefore, reducing noise is equivalent to minimizing ρ. Being defined in the bounded variation space, TV functionals allow for discontinuities in the image functions. Hence, regularization through TV promotes recovery of edges, which appear as “jumps” or discontinuous parts of the image, and effective noise removal. But studies have revealed that TV formulations favor piecewise-constant solutions, a consequence that generates staircase effects and introduces false edges [16]. Also, TV regularization tends to lower contrast even in noise-free or flat image regions [17].
In the similar notion of the TV principle, Perona and Malik proposed an energy functional, ϕ, defined by
where K denotes the shape-defining constant, which can be minimized to suppress noise [10]. Minimizing Eq. (8), which originates from robust statistics, produces a nonlinear diffusion equation that embeds a fractional conduction coefficient for preserving edges. The PM energy functional in Eq. (8) is nonconvex for |∇u| > K, an undesirable property that can generate instabilities in the evolving solution. This work presents a technique that retains the convex portion, |∇u| ≤ K, and complements the nonconvex portion of the PM potential by the TV energy functional.
The regularization process is often supported by the fidelity potentials
for additive noise, f = u + η, and
for multiplicative noise [18], f = uη, where f is the corrupted image and λ is the fidelity parameter that balances the trade-off between u and f. The fidelity term is often added to the regularization framework.
The hybrid model can be derived from the minimization problem that integrates the corresponding energy functionals from super-resolution, TV, PM, and fidelity. Assuming additive noise and L2 estimator for the super-resolution part, the (regularized) minimization super-resolution problem parametrized in α and β becomes
where α, β ∈ [0, 1] and
Eq. (12) offers both super-resolution image reconstruction and noise removal capabilities, dictated by TV and PM models. From the equation, as t → ∞ , u approaches an optimal solution—a stationary function that solves the energy functional, H, in Eq. (11). Eq. (12) has interesting properties for various parts of the image: in flat regions (|∇u| → 0), Eq. (12) reduces to
where C > 0 is a constant. This equation has a Laplacian term, Δu, which possesses isotropic diffusion characteristics to strongly and uniformly suppress noise in flat regions. In the neighborhood of the edges (|∇u| → ∞), Eq. (12) becomes
implying protection of edges against smoothing. This automatic interplay between reconstruction and regularization components helps to generate superior super-resolved images.
The solution of the proposed super-resolution model in Eq. (12) was iteratively estimated using the steepest descent method. Therefore, the evolution equation in Eq. (12) can be converted into a numerical system
where n denotes the iteration number that defines the solution space index of u, and τ > 0 denotes constant of the step size in the gradient direction. To encourage stability of the evolution equation in (15), the Courant-Friedrichs-Lewy condition, that is 0 < τ ≤ 0.25, should be satisfied [19]. From the equation, the degradation matrices, namely Wk, Bk,and Dk, and their corresponding transpose versions may be regarded as direct operators for image manipulations: shifting, blurring, and downsampling, along with the reverse of these operations [11]. With this observation of the matrices properties, implementation of the super-resolution component of Eq. (15) can be achieved using cascaded operators without explicitly constructing the operators as matrices. This implementation strategy helps to boost the algorithmic speed and to optimize hardware resources.
Eq. (15) can be represented in block form by Figure 4. From the Figure, each low-resolution frame, yk, is compared with the current estimate, un, of the high-resolution image. This process is undertaken by block Pk, detailed in Figure 5—an operator that represents the gradient back projection to compare the kth degraded frame and the high-resolution estimate at the nth iteration of the steepest descent method. Note from Figure 5 that T(PSF), with PSF denoting the point spread function, replaces
Block diagram representation of the proposed super-resolution model. The blocks Pk and Q are defined in Figures 5 and 6.
Extended block diagram representation of the similarity cost derivative, Pk, in Figure 4.
Block diagram representation of the smoothing cost derivative, Q, in Figure 4.
Several experiments were executed to determine performance of the proposed super-resolution model relative to the classical approaches. The methodology and procedures under which the experiments were undertaken can be explained as follows: firstly, high-resolution images of bike, butterfly, flower, hat, parrot, Parthenon, plant, and raccoon (Figure 7) were degraded to generate the corresponding low-resolution images (Figure 8, first column). Note that the original images were downloaded from the public domain with standard test images.1 These images were selected because they contain detailed features, and hence it would be easier to test the superiority of various super-resolution methods. As an example, the “Raccoon” image contains small-scale features (fine textures or fur) that most super-resolution approaches may find hard to restore. Degradation of the original images was achieved through warping, blurring, decimation, and noise addition to create sequences of 10 low-quality images with consecutive pairs differing by some rotation and translation motions. To void impacts of registration errors on the reconstruction process, the warping matrix was fixed. Thus, for 10 multiple low-resolution images, the warping matrix for the horizontal and vertical displacements, respectively denoted by ∆x and ∆y, was defined as follows:
∆x | 0.56 | 1.03 | 0.85 | 0.32 | −0.45 | −0.43 | 0.92 | 1.23 | 0.93 | 0.64 |
∆y | 0.12 | 0.53 | 0.27 | 0.00 | −0.83 | 1.12 | 1.08 | 0.12 | 0.54 | 1.37 |
Original high-resolution images.
Super-resolution results from different methods.
Next, super-resolution methods based on a variety of regularizers, namely NC00 [20], TV [9], ANDIFF [21], and Hybrid, were applied on the degraded images to restore their original versions. Lastly, the objective metric, namely feature similarity (FSIM) [22], and the subjective metric were used to compare performances of different methods. FSIM incorporates into its formulation some aspects of the human visual system, and hence the metric is considered superior over several other existing image quality metrics. A visually appealing image has a higher value of FSIM, and vice versa.
Visual results show that the classical methods tend to add undesirable artificial features into the reconstructed images (Figure 8). For instance, NC00 introduces bubble-like features around borders, edges, and corners, which are the critical features that emulate the human visual system. The method, on the other hand, does well on homogeneous image regions. The super-resolution method based on TV produces relatively sharper images, but the method also adds artifacts on homogeneous parts of the final images—an effect that degrades the visual quality of the images. The ANDIFF method generates smoother results that contain little artifacts, but the method underperforms for highly-textured images such as the Raccoon. The proposed hybrid model established a proper balance between smoothness and critical feature preservation (Figure 8, last column). Visually, the reconstructed images by our approach are more natural and are free from obvious artifacts. One may argue about a slight blurriness in our results. However, given the higher capability of the proposed method to preserve sensitive image features, this effect may be ignored. Also, the line graphs (taken near the last row across all columns) further confirm that the proposed method is superior because it generates a one-dimensional curve that closely matches the original one (Figure 9).
Line graphs of images generated by different super-resolution methods.
Numerical results demonstrate that, in all cases of the input images, the proposed super-resolution method achieves higher quality values (Table 1). These convincing objective observations can be explained well from the new formulation in Eq. (12): the hybrid super-resolution model captures the qualities of both PM and TV, an advantage that may promote higher objective quality results. Besides, our formulation incorporates parameters that give an effective interplay between the regularization functionals.
Image | NC00 | TV | ANDIFF | Proposed method |
---|---|---|---|---|
Bike | 0.7139 | 0.7148 | 0.7386 | 0.7642 |
Butterfly | 0.6721 | 0.6733 | 0.7386 | 0.7592 |
Flower | 0.6998 | 0.7084 | 0.7669 | 0.7970 |
Hat | 0.7512 | 0.7624 | 0.8106 | 0.8194 |
Parrot | 0.7672 | 0.7908 | 0.8595 | 0.8738 |
Parthenon | 0.7101 | 0.7287 | 0.7450 | 0.7618 |
Plant | 0.7429 | 0.7416 | 0.8230 | 0.8401 |
Raccoon | 0.7591 | 0.7877 | 0.8046 | 0.8257 |
Feature similarities of images restored from various super-resolution methods.
In this work, we have established a hybrid super-resolution framework that combines desirable features of TV and PM models. The framework has been parametrized to mask weaknesses of the models, introduce an automatic interplay between TV and PM regularizations, and promote appealing results. More emphasis was put on super-resolving low-quality images while retaining their naturalness and preserving their sensitive image features. Experimental results demonstrate that the proposed framework generates superior objective and subjective results.
Nowadays, microwave photonics (MWP) is a relatively mature scientific and technological direction arising among radio electronic R&D society at the second half of the twentieth century in result of combining the achievements of microwave electronics and photonics techniques [1]. Initially, MWP was an area of interest for a military platform [2, 3] such as radar and electronic warfare means, but recent years, it became an object of study and development for emerging areas in the telecommunication industry [4] such as fifth-generation (5G) cellular networks. For today, MWP technology might be considered as a perspective direction of modern radio electronics for signal generation, transmission, and processing in various radio frequency (RF) circuits and systems of microwave (MW) band. Implementation of this concept will enhance the key technical and economical features and such important characteristics as electromagnetic and environmental compatibilities, immunity to external interferences.
\nFollowing this tendency, we have contributed some works referred to computer-aided design of MWP components and MWP-based devices [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18] using two well-known software tools such as VPI Photonics Design Suite (VPI-PDS) [19] and Applied Wave Research Design Environment (AWRDE) [20]. Elaborating the direction, in this chapter, we review shortly the distinctive features of MWP technique, preselecting an optimal software to computer-aided design (CAD), a hybrid device combining microwave electronics and photonics components. After that, we highlight our last modeling and simulation results on design and optimization of advanced microwave and millimeter-wave band RF electronic facilities based on MWP technique, mainly for an access network of 5G mobile communication systems. In particular, Section 2 reviews the nature, features, and space of the MWP approach to develop advanced radio electronics apparatuses (REAs). In addition, Section 3 presents a short comparative analysis of modern computer platforms with the goal of selecting a feasible mean to design MWP-based REA. The examples for comparative computer-aided simulations of key optical and optoelectronics elements, such as laser, optical modulator, photodetector, and optical fiber, as well as based on them specific MWP devices and apparatuses for microwave-signal processing in optical range such as a delay circuit, oscillator, frequency converter, and fiber-wireless fronthaul of 5G mobile communication system, are demonstrated in Section 4. All schemes are simulated in VPI-PDS and AWRDE CAD tools. Finally, Section 5 concludes the chapter.
\nMicrowave photonics is a rather fresh interdisciplinary scientific-technical and scientific-technological direction of radio electronics and photonics, which provides an increase in the efficiency of the formation and processing of analog and digital radio signals due to their transfer to the optical range. The use of MWP in promising radio facilities for various purposes has the potential, first, from the point of view of increasing operating frequencies up to tens of terahertz, ensuring their multirange, multifunctionality, reconfigurability, and increasing speed and throughput in accordance with modern requirements. Another purpose of MWP is to improve the performance characteristics of existing REAs such as instantaneous bandwidth, electromagnetic compatibility, power consumption, reliability, resistance to natural and intentional interference, footprint, and environmental friendliness.
\nGenerally, MWP devices are the examples of an intimate integration of photonics, microwave electronics, and planar antenna technologies for producing a complicated functional module in a multichannel analog environment. In particular, MWP technology opens the way to superwide bandwidth transmitting characteristics at lower size, weight, and power as compared with traditional electronic information and communication systems [2, 3]. For example, it is expected that this direction will find wide application in the RF equipment for accessing networks of incoming mobile communication systems with distribution in the millimeter-wave range [4, 17]. Figure 1 demonstrates a typical MWP arrangement, where for direct and inverse transferring of MW and optical signals, two interfacing units are allocated at their bounds: MW-to-optical (MW/O) and optical-to-MW (O/MW) converters. Between the interfaces, there are various photonics processing units for transmission, switching, distribution, filtration, time delaying, amplification, and frequency conversion of microwave signals in optical domain.
\nA typical arrangement of MWP circuit.
In the process of design, a developer of new MWP-based REA is facing a problem of choosing an appropriate software tool. As of today, the existing optical and optoelectronic CAD tools (OE-CAD) based on so-called Photonic Design Automation (PDA) platform are not developed like CAD tools intended for modeling of RF and MW circuits (MW-CAD) based on so-called Electronic Design Automation (EDA) platform that have been underway for about 5 decades. So today, to solve the problems of successful introducing MWP technique to the next-generation REAs, their individual units, and devices, there are various PDA-based CAD systems that allow creating complex models of varying difficulty. In general terms, all specialized CAD systems can be divided into a group for the structural design of optical linear and nonlinear media on various materials and a group for system modeling, in which individual devices are introduced as closed models with a set of specific characteristics.
\nFollowing it, currently, some commercial CAD systems have been developed for modeling optical and optoelectronic devices and systems based on PDA platform. The most popular representatives are VPI Photonics Design Suite from VPI Photonics, OptiSystem from Optiwave Design Software, and so on. However, our design experience in such OE-CAD systems clearly showed that they are most applicable for modeling complex apparatuses and systems, rather than individual device. In particular, the models of optical and optoelectronic components studied below are presented in the VPI-PDS tool in the form of ready-made library models with a very limited number of parameters necessary for their development. Therefore, based on this software, it is impossible to carry out detailed modeling of their functioning. For example, it is impossible to calculate a transfer characteristic in the large-signal mode taking into account introduced nonlinear distortions and also an influence of spurious elements of the input/output circuit and chip construction in the MW band.
\nTo overcome this serious drawback, we almost 10 years ago proposed a different approach using a device-oriented MW-CAD tool [5], which was subsequently expanded in Ref. [13]. Its essence is that the optimal solution to the problem of modeling MWP components and MWP-based devices according to the criteria of accuracy and time-of-decision should be based on a rational combination of structural [in the form of an physical equivalent circuit (FEC)] and structureless models (when the response of the device is described in frequency, temporal, and spatial areas based on external input and output characteristics) of circuit elements. The effectiveness of this approach, called end-to-end multiscale design, has been confirmed experimentally, for example, when modeling optoelectronic devices with a MW passband [21]. Below, we briefly characterize both classes of CAD tools using the example of AWRDE and VPI-PDS.
\nThe AWRDE is a comprehensive EDA platform for developing RF/microwave products that provide radio engineers with integrated high frequency, system, and electromagnetic (EM) simulation technologies and design automation to develop physically realizable electronics ready for manufacturing. The tool helps designers manage complex integrated circuit (IC), package, and printed-circuit board modeling, simulation, and verification, addressing all aspects of circuit behavior to achieve optimal performance and reliable results for first-pass success. The unique AWRDE tool features are the following:
Unified design capture provides front-to-back physical design flow with dynamically linked electrical and layout design entry. Components placed in an electrical schematic automatically generate a synchronized physical layout based on libraries of standard, customized, and/or vendor-provided components.
Design flow supports complex hierarchical projects with parameterized subcircuits for easy optimization and tuning. Circuit, system, or EM-based subcircuits can be quickly developed and used to populate larger, more complex networks common in today’s RF front-end circuitry.
Interoperability with industry-standard tools enables the exchange of design data for schematic or netlist import, bidirectional EM cosimulation, electrical or design rule check, and production-ready export. Additionally, powerful yield analysis and optimization address manufacturing tolerances for more robust designs and greater profitability.
Customization due to the powerful application-programming interface extends the capabilities of the software using popular programming languages, providing user-defined scripts for automating common or complex tasks and custom design flows.
VPI-PDS sets the industry standard for end-to-end PDA comprising design, analysis, and optimization of components, systems, and networks that provide professional simulation software supporting requirements of active/passive integrated photonics and fiber optics applications, optical transmission system and network applications, and cost-optimized equipment configuration. The unique VPI-PDS tool features are the following:
Link engineering solutions provide simple means for the cost-effective optical network configuration and offer a unified approach to control equipment libraries and engineering methodologies.
Transmission design solutions provide professional means for investigating and optimizing system technologies and evaluating novel component and subsystem designs in a system context.
Component design solutions provide professional means for the development and optimization of photonic ICs, optoelectronic components, and fiber-based amplifiers and lasers.
Device simulation solution provides a versatile simulation framework for the analysis and optimization of integrated photonic waveguides and optical fibers.
In process of development of such MWP REAs combined microwave and photonic circuits, there was a problem to use an optimum computer product for their modeling and design. The essence is that for the accurate solution of an issue for modeling of such complicated systems containing radio engineering and optical elements and devices, the specialties of their functioning in both ranges must be taken into consideration. In this regard, more than 20 years ago, the conclusion was drawn that the optimal way for increasing the accuracy of MWP circuits taking into account the influence of their parasitic elements in MW band requires use of the high-power MW-CAD tool working at the symbolical level [19]. Table 1 lists the detailed comparison of typical modern OE-CAD tool VPI Photonics Design Suite of VPI Photonics and well-known MW-CAD tool AWRDE of Cadence.
\n# | \nFeature | \nRealization | \n|
---|---|---|---|
By MW-CAD (AWRDE) | \nBy OE-CAD (VPI-PDS) | \n||
1 | \nAnalysis approach | \nBuilding blocks, 3D electromagnetic analysis | \nBuilding blocks | \n
2 | \nSimulation methods | \n\n | \n |
Linear circuits | \nS- and Y-matrices, equivalent circuits | \nS-matrices | \n|
Nonlinear circuits | \nHarmonic balance engine ALPAC, 3D planar electromagnetic simulator AXIEM modeling | \nS-matrices, combination of time-and-frequency domain modeling | \n|
3 | \nElement representation | \n\n | \n |
Active microwave elements | \nMultirate harmonic balance, HSPICE, Volterra, based on measured characteristic models | \nIdeal or based on measured characteristic models | \n|
Active MWP elements | \nAbsent | \nRate equation-based, transmission line models | \n|
Passive elements | \nLumped and distribution, microwave band specialties | \nLumped, ideal | \n|
4 | \nPossibility for calculating the key parameters of MWP circuits and links | \nBy one-click operation | \nBy user-created complicated schemes | \n
5 | \nIC layout design and analysis | \nYes | \nNo | \n
6 | \nBuilt-in design kits from the main foundries | \nYes | \nNo | \n
7 | \nParameter optimization | \nYes | \nNo | \n
8 | \nSensitivity analysis | \nYes | \nNo | \n
9 | \nDesign of tolerance | \nYes | \nNo | \n
10 | \nStatistical design | \nYes | \nNo | \n
11 | \nYield optimization routine | \nYes | \nNo | \n
12 | \nBuilt-in library of producer-specific models | \nYes | \nNo | \n
Comparison of modern ME-CAD and OE-CAD tools.
In result, the following outputs to optimally design the MWP-based REAs can be drawn out:
The available OE-CAD platform is most applicable for analyzing complex devices and systems, rather than their individual components, which are presented in the form of parameterized or formal library models with a very limited number of parameters necessary for accurate development of MWP-based REAs. In particular, MW REA’s passive elements such as waveguides, couplers, resonators, resistors, capacitor, and inductor represent only by ideal lumped models. In addition, calculating the key parameters of MWP circuits and links, such as large-signal transmission gain, noise figure, phase noise, intermodulation distortion, and intercept points is possible only by user-created complicated testbeds. While on MW-CAD platform, they are calculated using a ‘one-click’ operation.
From the developer\'s point of view, the OE-CAD platform lacks (or is just starting to appear) a large number of functions that are very useful for investigating the device under design (see items 5–12 of Table 1).
The main disadvantage of the MW-CAD platform is the lack of models of active optoelectronic components such as semiconductor lasers, photodiodes, and electro-optic modulators.
Our multiyear experience in CAD of MWP devices using AWRDE tool has shown that the most convenient way to introduce optoelectronic devices is to present them as a behavioral model in the form of a nonlinear physical equivalent circuit. In this circuit, the linear section is built on the basis of passive lumped or distributed components, and the nonlinear one uses sources (current, voltage, noise, etc.), the characteristics of which are based on experimental data.
Having clarified the principal pros and cons of the two classes of software tools from the point of view of designing MWP-based REAs, in this section, we exemplify specifically the results of their comparative calculation for various devices and systems.
\nTo conduct accurately comparative modeling of MWP REAs, it is necessary to perform a reciprocal calibration for the models of optoelectronic and optical components. In this regard, the behavioral models in the AWRDE are initially more accurate, since they are based on experimental data. That is, the calibration consists in fitting the parameters of the VPI-PDS models so as to obtain close basic characteristics in small- and large-signal modes. Below, we present and discuss the results of model calibration for key optoelectronic and optical components, based on which a set of subsequent simulations for basic REAs will be carried out in the next subsection.
\nVariants of AWRDE-based semiconductor laser source (SLS) model in the form of FECs are proposed and described in detail in Refs. [5, 6, 13, 18]. On the other hand, there are more than 10 library models of SLS in VPI-PDS tool mainly based on linear or nonlinear rate equations differing in the way they are presented and in the set of input data. Figure 2 exemplifies the result of small-signal frequency response (mod. S21) simulations using AWRDE’s single-carrier model [18] and VPI-PDS’s “LaserRateEqSM.vtms” model. As follows from the figure, both graphs for this reciprocally calibrated optoelectronic element have a similar appearance with typical conversion losses of about 30 dB, about 3-dB rise associated with the so-called electron-photon resonance, and −3-dB direct modulation bandwidth of slightly larger than 11 GHz.
\nSmall-signal frequency response of the semiconductor laser source model by (a) AWRDE and (b) VPI-PDS.
The AWRDE-based electro-optical intensity modulator (EOM) model of so-called electroabsorption type in the form of FECs is proposed and described in detail in Ref. [13]. On the other hand, there are two library models of electroabsorption modulator (EAM) in VPI-PDS tool differing in the way they are presented and in the set of input data. Figure 3 exemplifies the result of large-signal optical spectra simulations using AWRDE model [13] and VPI-PDS’s “ModulatorEA_Polynomial.vtms” model. As follows from the figure, both graphs for this reciprocally calibrated optoelectronic element have a similar appearance with approximately the same power levels of the fundamental signal and the first two harmonic distortions caused by the nonlinearity of the modulator’s transfer characteristic.
\nLarge-signal optical spectrum of the electro-optical modulator model by (a) AWRDE and (b) VPI-PDS.
Variants of AWRDE-based pin-photodiode (PD) model in the form of FECs are proposed and described in detail in Refs. [7, 8, 13, 18]. On the other hand, there are only one unified model of PD in VPI-PDS tool that is ideal and handles both single-mode and multimode optical signals. Figure 4 exemplifies the result of small-signal frequency response simulations using AWRDE model [13] and VPI-PDS’s “Photodiode.vtms” model. As follows from the figure, both graphs for this reciprocally calibrated optoelectronic element have a similar appearance. However, using the same reference data, a −3-dB bandwidth was obtained a little more than 20 GHz for the AWRDE model and 27 GHz for the VPI-PDS model.
\nSmall-signal relative frequency response of the PIN-photodiode model by (a) AWRDE and (b) VPI-PDS.
The most probable reason for this meaningful discrepancy is explained by the ideality of the VPI-PDS model, which does not take into account the influence in MW band of either the photodiode chip itself or the parasitic elements of its output circuit. Specifically, in order to obtain a reasonable decrease in the frequency response at higher frequencies, a library model of a low-pass filter had to be introduced at the PD model output. Effect referred to parasitic circuit elements may be clearly explained by Figure 5. It follows from the AWRDE graphs that a designer can realized twofold expansion of the PD’s 3-dB passband (20–40 GHz) owing to the appropriate fitting of the connecting wire inductance L\nw.
\nEffect of the connecting wire between photodiode chip and output pad.
In general, with the wave approach, where light is regarded as an EM wave, any optical passive element, including the optical fiber (OF), can be simulated in the same way in MW-CAD or in OE-CAD tool. Namely, in AWRDE, a segment of optical fiber of a certain length can be equivalently represented using, for example, the library model of physical transmission line with loss (TLINP). However, when constructing a realistic model of an OF, a whole set of additional effects should be taken into account, such as dispersion, reflection, scattering, nonlinearity, and ambient temperature, the influence of which can degrade the transmission characteristic. The AWRDE-based OF model in the form of FECs taking into account the above limiting factors is proposed and described in detail in Ref. [15]. On the other hand, there are as many as nine library models of multimode or single-mode OF in VPI-PDS tool differing in the way they are presented, which deteriorating factors and what set of input data are taken into account. Figure 6 exemplifies the result of small-signal phase response (arg. S21) simulations using AWRDE’s simplified model [18] and VPI-PDS’s “UniversalFiberFwd.vtmg” model. As follows from the figure, both graphs for this reciprocally calibrated optical element have a similar appearance and the same slope.
\nRelative phase-frequency response of the single-mode optical fiber model by (a) AWRDE and (b) VPI-PDS.
The purpose of this subsection is to generalize the results of the reciprocal calibration for optical and optoelectronic component models in such a way as to provide unified reference data on their parameters for further studies. Table 2 lists the common reference data for four above-considered models of SLS, EOM, PD, and OF as well as of electronic amplifier typically used after pin-PD.
\nParameter | \nValue | \n|
---|---|---|
Semiconductor laser source | \nOperating current | \n40 mA | \n
Average power | \n8 mW | \n|
Optical carrier | \nC-band (191 to 196.1 THz) | \n|
Linewidth | \n1.5 MHz | \n|
Relative intensity noise | \n−150 dB/Hz | \n|
Threshold current | \n8.5 mA | \n|
Slope efficiency | \n0.14 W/A | \n|
Direct modulation 3-dB bandwidth | \nUp to 11 GHz | \n|
Electro-optical modulator (EAM) | \nOperating voltage | \n−0.6 V | \n
Extinction ratio | \n14 dB | \n|
Slope efficiency | \n0.14 W/V | \n|
Linewidth enhancement factor (α) | \n1.0 | \n|
3-dB modulation bandwidth | \n30 GHz | \n|
PIN-photodiode | \nResponsivity | \n0.7 A/W | \n
Dark current | \n100 nA | \n|
Optical input power | \n<3 mW | \n|
3-dB passband | \nUp to 30 GHz | \n|
Post-amplifier (if needed) | \nGain | \n40 dB | \n
Noise spectral density | \n20 × 10−12 A/Hz1/2\n | \n|
Optical fiber | \nType | \nSMF-28e+ | \n
Length | \nUp to 20 km | \n|
Attenuation | \n0.2 dB/km | \n|
Dispersion | \n17 e−6 s/m2\n | \n|
Dispersion slope | \n80 s/m3\n | \n
Reference data of elements for the further study.
In this subsection, the subjects of the study are the specific microwave photonics (MWP) devices and apparatuses such as a delay circuit, oscillator, frequency converter, and fiber-wireless fronthaul of 5G mobile communication system. The tools for the comparative computer simulation are well-known commercial software AWRDE and VPI-PDS. The research takes into account some key distortion sources of the MW signal under processing such as introduced noise and nonlinear distortion of active optoelectronic elements as well as chromatic dispersion of the optical fiber. The parameters for the elements to be used are based on the data of Table 2.
\nFiber-optic delay circuit is one of the most feasible MWP units [22]. Figure 7 shows the block diagram of the single-channel optical delay circuit (ODC) under test including semiconductor laser that directly modulated by input MW signal, optical fiber, the length of which corresponds to the required delay time, and a photodetector, at the output of which a delayed MW signal is formed. Following it, below we will describe two models and some comparative simulation results using AWRDE and VPI-PDS tools.\n
\nBlock diagram of the optical delay circuit under test.
\nFigure 8 demonstrates the model for the simulation experiment evaluating some key quality parameters for ODC under test when transmitting continuous wave MW signals. As one can see, it contains the same ODC layout as in Figure 7consisting of the calibrated in the previous subsection library model for single-mode laser, so-called galactic model for optical fiber also including delay element, and library models for pin-photodiode and electrical post-amplifier.
\nVPI-PDS’s model of fiber-optic delay circuit of MW signals.
The layout of single-channel ODC [15] is very simple and contains (Figure 9) the subcircuit models of SLS, single-mode optical fiber of a corresponding length (delay ≈4.8 ns/m), and PD.
\nAWRDE model of fiber-optic delay circuit of MW signals.
\nFigure 10 exemplifies the simulation results for ODC’s group time delay (GTD), where the MW signal frequency is swapped in the range of 1–7 GHz, and the OF length is 3 m. As follows from the figure, due to the broadband of the constituent elements, the delay does not change in such a wide frequency range of modulating frequencies (almost 3 octaves). Its value coincides with high accuracy for both models and is close to the above delay in a standard single-mode fiber. In addition, Figure 11 demonstrates the large-signal amplitude characteristic of the ODC under test. As one can see from the figure, 1 dB input compression point is near −10 dBm for the both models.
\nExamples of the simulation results for FODC of MW signals: relative phase-frequency response by (a) AWRDE and (b) VPI-PDS.
Simulated large-signal power characteristic for the FODC of MW signals under test by (a) AWRDE and (b) VPI-PDS.
The following outputs can be drawn from our study:
The investigated optoelectronic delay circuit is a very simple device that, in contrast to the electronic analog, provides an extremely wide operating bandwidth and, thanks to the very short delay time in electro-optical and optical-electric converters and low losses in an optical fiber, an extremely wide delay range from units of nanoseconds to hundreds of microseconds.
Both computer tools under study provide approximately the same accuracy of calculations, which coincide with the actual value of the delay in the fiber [22]; however, the AWRDE model is simpler and more flexible.
\nFigure 12 presents the block diagram of the MW signal’s optoelectronic oscillator (MW-OEO) that is another worldwide example of MWP application [23]. Generally, it contains two requisite sections: optical one and electrical one. Here, the optical section includes SLS, EOM, OF, and PD. The electrical section includes low-noise MW amplifier (LNA), band-pass filter (BPF), power MW amplifier (PA), and electrical coupler (EC).
\nBlock diagram of the MW-OEO under test.
Following a similar approach as in our previous computation modeling, Figure 13 shows a VPI-PDS model of MW-OEO [10]. An important specificity of this model is in taking a phase noise of SLS into consideration.
\nVPI-PDS’s model of optoelectronic oscillator of MW signals.
Note that due to the absence in this software the library model of optical fiber (OF) that takes into account the delay in it, the OF model in the diagram has been replaced by library models of the optical attenuator and the delay element with identical parameters.
\n\nFigure 14 represents circuit-level nonlinear model of the MW-OEO under study realized by AWRDE software. The diagram includes a chain of subcircuits (SUBCKTs) representing (from left to right): small-signal (including noise) and large-signal features of SLS (see Section 4.1.1), delay and losses of OF, nonlinear optical-to-electrical conversion feature of PD (see Section 4.1.3), gain and bandwidth of LNA, bandwidth and losses of BPF, frequency and amplitude features of PA, and couple of EC models realized by AWRDE tool. Besides, there are two service program elements mitigating self-sustained oscillation in the return path of the model: ideal DGDELAY that models an ideal, linear, frequency-dependent, digital time delay element and OSCAPROBE that initiates a large-signal oscillator simulation.
\nAWRDE’s model of optoelectronic oscillator of MW signals.
As an example, Figure 15 presents phase noise characteristics for MW-OEO of 9 GHz simulated by the OE-CAD tool (black line) and by the MW-CAD tool (red curve). As one can see, there is a significant discrepancy in the simulation results at the offsets more than 100 kHz.
\nPhase noise characteristics (RIN = −150 dBc/Hz).
The following outputs can be drawn from our study:
With small offsets from the MW carrier, the phase noise levels calculated using both software approximately coincide with each other and with experimental data [9].
With large offsets, the discrepancy between the AWRDE-calculated and experimental data does not exceed 2 dB [9], which indicates the more validity of its model.
To measure the phase noise of an oscillator, there is a built-in model of the noise analyzer (OSCNOISE) in the AWRDE tool, while to perform this operation in the VPI-PDS tool, it is necessary to create a complex testbed.
About 10 years ago, we proposed a simple circuit for an optoelectronic frequency converter (OEFC) of MW signals, in which the nonlinearity of a SLS’s light-current characteristic is leveraged [11]. The efficiency of this device was confirmed by modeling in VPI-PDS and experimental research at input frequencies of 1 and 1.5 GHz. Later, the operation of this device was modeled in AWRDE tool at other frequencies of the MW input signals [10]. The block diagram of the OEFC containing an electronic power combiner mixing the RF and LO MW-signals, a SLS, a pin-PD, and an electronic bandpass filter to isolate the mixing product is shown in Figure 16.
\nBlock diagram of the MW-OEFC under test.
Following a similar approach as in our previous computation modeling, Figure 17 depicts a VPI-PDS model of MW-OEFC [11]. Its appearance repeats the diagram of Figure 16 with the introduction of an electronic attenuator (El), which serves to adjust the level of MW signals at the input of the SLS.
\nVPI-PDS’s model of optoelectronic frequency converter of MW signals.
Following the above block diagram, Figure 18 demonstrates the OEFC model under investigation in AWRDE environment. This figure includes a chain of subcircuits representing (from left to right) SLS (first three sections) and pin-PD (right sections) nonlinear models realized by AWRDE tool (see Sections 4.1.1 and 4.1.3). The laser model is presented by the FEC of the linear sections of the SLS model (S2) together with the test fixture model (S1) and nonlinear section (A1) representing AWRDE’s library element LOOKUP that implements a lookup table including its measured light-current characteristic. The right section of the chain is nonlinear PD FEC model. Simulation details are reported in Ref. [10].
\nCircuit-level AWRDE’s optoelectronic MW-frequency converter model.
\nFigure 19 shows the results of simulation experiment referred to defining output spectra of the OEFC under investment by AWDE MW-CAD tool (a) and VPI-PDS OE-CAD tool (b). In both procedures, the input RF signal had a power of −20 dBm at a frequency of 1 GHz, and LO signal had a power of 6 dBm at a frequency of 1.5 GHz.
\nLarge-signal optoelectronic MW frequency converter output spectra by (a) AWRDE and (b) VPI-PDS.
The following outputs can be drawn from our study:
As one can see from Figure 19(a), applying powerful harmonic balance method of AWRDE software resulted in output (IF) signal power near −55 dBm at a frequency of 2.5 GHz, that is, conversion gain is −35 dB. The rest of the peaks in the figure represent clearly the full output spectrum of standard microwave mixer in agreement with well-known formula |mFRF ± nFLO\n|, where m and n are integers. On the other hand,Figure 19(b) shows a comparable result referred to conversion gain, however, a significant part of the mixing products either differs in level or is absent altogether.
The results of simulation using the proposed AWRDE models should be closely matched to the experimental ones because their parameters are constructed on the measured characteristics of laser and photodiode.
In the framework of 5G’s Radio-over-Fiber (RoF) concept, fiber-wireless fronthaul network (FWFN) is one of the promising ways to deliver intensive digital traffic with seamless convergence between wired optical backhaul and fiber-wireless fronthaul, which is important to keep the remote cells flexible, cost effective, and power efficient [4, 17]. The block diagram of the FWFN containing Central station (CS) and a set of Remote stations (RS) interactively connected to CS via fiber-optics links (FOL) is shown in Figure 20. A typical position of RS is in the center of the service area; that is, for omnidirectional covering, four phased array antennas with an azimuth of 90° would be an optimal decision [14, 16].
\nBlock diagram of the fiber-wireless fronthaul network under test.
\nFigure 21 depicts the VPI-PDS’s model of downlink channel for FWFN under study that has the same block diagram as in Figure 20.
\nVPI-PDS model of downlink channel for a fiber-wireless fronthaul network.
As one can see from the figure, there are three parts such as CS, FOL, and RS. The first one includes the set of library models imitating quadrature amplitude modulated (QAM) MW transmitter as well as the models of SLS and EOM calibrated in Section 4.1. The second one consists of the library model of polarization controller and the model of OF calibrated in Section 4.1. Finally, the third one includes the model of pin-PD calibrated in Section 4.1 as well as the set of library models imitating QAM MW receiver. A detailed description of the QAM transmitter and receiver models is given in Ref. [15].
\n\nFigure 22 depicts AWRDE’s model of downlink channel for FWFN under study. The model has the same arrangement as in Figure 21 excluding the transmitting part that contains the library model of quasi-optical tone generator imitating laser carrier, the library model of multiplexer that performs the operation of upconverting signal to the optical range, and a passive subcircuit representing frequency response of the EOM under test in S2P format. Note that earlier we proposed and described in detail [13] a nonstructural nonlinear model for the EOM of the EAM type suitable for developers of local telecommunication systems based on RoF technology. However, here, its simplified model with the parameters calibrated in Section 4.1 is used.
\nAWRDE model of fiber-wireless fronthaul network. 1, QAM generator; 2, MW tone generator; 3, multiplexer; 4, quasi-optical signal generator; 5, behavioral mixer; 6, optical frequencies splitter; 7, MW noise generator; 8, model of single-mode fiber as subcircuit; 9, model of photodiode as subcircuit; 10, post-amplifier; 11, signal delay compensator; 12, vector signal analyzer.
In this section, the subject of the study is a MWP-based FWFN; the devices of study are SLS, EOM, single-mode OF, and PD, which parameters have been calibrated in Section 4.1. The tools for the computer simulation are two well-known commercial program environments such as OE-CAD VPI-PDS and MW-CAD AWRDE. The study took into account the key distortion sources of the transmitted signal: noises of the laser, chirp of the modulator, and losses and chromatic dispersion of the fiber. To eliminate the influence of nonlinear effects during modulation and signal transmission through the fiber, MW and optical signal levels were selected, so that the modulation index did not exceed 30%, and the optical power in the fiber was below 5 mW.
\n\nTable 3 lists the common reference data for the simulation experiment.
\nParameter | \nValue | \n
---|---|
Length of pseudo-random bit sequence | \n215–1 | \n
Bitrate | \n2.5 Gbit/s | \n
RF carrier frequency | \n25 GHz | \n
Input RF power | \n−11 to −26 dBm | \n
Type of RF modulation | \n16-QAM | \n
Type of optical modulation | \nIntensity | \n
Common reference data for the FWFN under study.
In preparation for the simulation experiments, the modulation index of each device under study was optimized in such a way as to ensure the maximum output MW carrier-to-noise ratio while maintaining the low-signal mode at the modulating frequency. Figure 23 depicts an example of comparative simulation of Error Vector Magnitude (EVM) versus fiber length characteristics for the FWFN under study during transmission of 2.5 Gbit/s and 16-QAM MW signal at the frequency of 25 GHz using signal-to-noise ratio (SNR) of 50 and 25 dB. For the best vision, there is the inset in the figure showing constellation diagrams at the fiber length of 10 km. In addition, the dotted line indicates the standard limit of the EVM during transmission of the 16-QAM signal, which is 12.5%.
\nEVM versus fiber length characteristics.
The following outputs can be drawn from our study:
the EVM versus fiber length characteristics simulated by both the software closely coincide with each other at the signal-to-noise ratio of 50 and 25 dB within the FOL distance of up to 10 km and
for longer FOL lengths, all characteristics show a peak that exceeds the standard limit, caused by the effect of chromatic dispersion [22].
The chapter is devoted to recovering the optimal principle to computer-aided design a new class of microwave band radio electronic apparatuses using microwave-photonics approach to effectively generate, transmit/receive, and process super wideband radio signals in near infrared optical range meeting minimum insertion loss of a quartz light guide. Preselecting a feasible software instrument to design MWP-based radio engineering apparatuses showed that up to date, exploiting for some decades microwave band software tools based on electronic design automation platform are preferable than relatively rudimentary software tools based on photonic design automation platform due to much more possibilities to produce the state-of-art radio engineering devices, apparatuses, and systems. In addition, the problem referred to the reasonableness and accuracy of calculations comes to the fore because in the second tool all active and passive electronic and photonic circuit elements are presented as ideal models with lumped parameters that do not take into account frequency distortion due to spurious elements and transmission lines with distributed parameters. To clear the fact and estimate the impact, a comparative modeling for four basic radio electronic apparatus designed on the microwave-photonics approach, such as optical delay circuit, optoelectronic oscillator, optoelectronic frequency converter, and 5G\'s fiber-wireless fronthaul link, was carried out using two widespread off-the-shelf software: VPI Photonics Design Suite (VPI-PDS) and Applied Wave Research Design Environment (AWRDE). The following outputs can be derived, which a developer should take into consideration. The advantage of the simulation in VPI-PDS software is its greater convenience and speed with acceptable calculation accuracy since the built-in library models of optoelectronic and optical components are mainly used. On the other hand, the gain of the simulation in AWRDE software is a more sophisticated and, at the same time, a more accurate characterization because their parameters are constructed on the measured characteristics of active optoelectronic components, so the results should be closely matched to the experimental ones. Our future work will focus on the upgrading already proposed models and designing new AWRDE models of devices and units for microwave photonics applications.
\nThis work was supported by the Russian Foundation for Basic Research, Grant Nos. 17-57-10002 and 18-29-20083.
\nThe authors declare the lack of the ‘conflict of interest’.
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\\n\\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\\n\\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nOAI-PMH
\\n\\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\\n\\nLicense
\\n\\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\\n\\nPeer Review Policies
\\n\\nAll scientific works are Peer Reviewed prior to publishing. Read more
\\n\\nOA Publishing Fees
\\n\\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\\n\\nDigital Archiving Policy
\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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