Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
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We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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\n
1. Introduction
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Entomophagy is prevalent in many regions, and ~1500–2000 species of insects and other invertebrates are consumed by 3000 ethnic groups across 113 countries in Asia, Australia and Central and South America [1]. Africa, where more than 500 species are consumed daily, is a hotspot of edible insect biodiversity [2, 3]. In Thailand, entomophagy has spread to the south from the north-east as people migrate towards city centres. It has become so popular that >150 species are sold in the markets of Bangkok [4]. The most common edible insects are moths, cicadas, beetles, mealworms, flies, grasshoppers and ants [5]. Although human insectivory is an ancient practice and 80% of the world’s population consumes insects, it is relatively uncommon in contemporary Western culture. In many regions that have traditionally eaten insects, the practice is declining due to globalisation, and their consumption has decreased over the last decade as agriculture and living standards change, and the availability of wild-caught insects has decreased [6, 7, 8].
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This chapter reviews and provides an accessible synthesis of the literature surrounding the potential of insects to alleviate food security while promoting food sovereignty and integrating social acceptability. These are immediate and current problems of food security and nutrition that must be solved to meet the Sustainable Development Goals [3, 9].
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\n
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2. Food insecurity
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Food insecurity is created when food is unavailable, unaffordable, unevenly distributed or unsafe to eat. Inefficiencies in the current food production system generate inconsistencies between the demand and supply of food resources, which is exacerbated by the diminution of pastures and increasing demand for food. Thirty percent of land is already used for agriculture, but 70% of this is used for macro-livestock production, an industry which consumes 77 million tonnes of plant protein only to produce 58 million tonnes of animal protein per year. This animal protein is not evenly distributed across the globe, as the average person in a ‘developed’ country consumes 40 g more protein a day than the average person in a ‘developing’ country [10]. The demand for affordable and sustainable protein is high, while animal protein is becoming more expensive and less accessible in some regions, especially in Africa [11].
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To ensure food access and to alleviate poverty, there is a particular need for investment into Africa’s agricultural potential as this continent will soon account for 50% of the world’s population growth. Currently, Africa has 25% of all undernourished people worldwide, and the income gap between rural and urban areas drives rapid urbanisation; this is decreasing the agricultural workforce [12, 13]. With substantial food insecurity and rising food prices, one in six people dies from malnutrition and hunger, and more than 1 billion people are undernourished, triggering 1/3 of the child disease burden [10, 14]. Effects are worse in the populations that already have high rates of malnutrition, such as Zambia, where chronic undernutrition is 45% and causes 52% of deaths in the population under the age of 5. Over 800 million people are thought to have a food energy deficit average of >80 kcal/day/person [3, 15].
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The prospect of global food shortage grows as the world’s population is estimated to increase to 9 billion by 2050. The conventional meat production system will not be able to respond sufficiently to the increase in demand. Per capita, meat consumption is expected to increase by 9% in high-income countries by 2030, and the increase in world crop prices will increase the price of meat by 18–21% [16]. Systems with a low carbon footprint must be promoted according to the economic and cultural restraints of the region by modifying animal feed from soy meal to locally sourced feed [17]. Any expansion of agricultural land must be mitigated to reduce losses in natural ecosystems. Therefore, our increasing population will need to be fed from the same area of land available now [18].
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Climate change is also a growing threat to global food security as this is reducing the area of land available to agriculture [10], and future cereal yields are predicted to decrease, especially in low-latitude areas. The poorest countries will suffer the worst consequences of climate change, which will increase both malnutrition and poverty. To prevent future undernutrition and to decrease current levels, food access and socioeconomic conditions must improve globally [14]. With this climate change-driven prediction of reduced agricultural yields in most countries given current crop practices and varieties, it is therefore necessary to increase the diversity and sustainability of crop supply so that food insecurity is not exacerbated [15].
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\n
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3. Nutritional potential of edible insects
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In general, insects have a higher quality of nutrition than macro-livestock in terms of protein, lipids, carbohydrates and vitamins [10]. Insects have high crude protein levels of 40–75%, contain all essential amino acids, are rich in fatty acids and have a high proportion of dietary fibre, and it has been further suggested that there are health benefits from eating chitin through enhancement of gut flora and antibiotic properties, though it is not known how insect fibre specifically affects human health [19].
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In a study of the calorific value of 94 insect species, 50% were higher than soybeans, 87% higher than maize, 63% higher than beef and 70% higher than fish [10]. The composition of omega-3 and omega-6 fatty acids in mealworms is comparable to that of fish, and other insects with ideal fatty acid ratios are house crickets, short-tailed crickets, Bombay locusts and scarab beetles [20]. Some insect species have micronutrients not found in some conventional animal proteins, such as riboflavin in termites and high concentrations of thiamine in silk moth larvae (224.7% daily human requirement) and palm weevils (201.3%) compared to chicken (5.4%). Mealworms have a higher content of protein (all essential amino acids), calcium, vitamin C, thiamine, vitamin A and riboflavin per kg than beef. Although the nutritional content of many insects is well-described in the literature, there is a variation depending on diet, sex, life stage, origin and environmental factors, and the realised nutritional content also depends on preparation and cooking [21, 22, 23].
\n
Insect consumption has the potential to reduce hunger on a global scale as they are nutrient dense as well as calorie dense. A calorie deficit of 1500 kcal/day could be addressed by rearing 1 kg/day of crickets in 10 m2 while also providing the recommended daily amount of lysine, methionine, cysteine, tryptophan, zinc and vitamin B12. Not only do insects provide calories and nutrients, but they are also cost-effective, easily grown and can be environmentally sustainable when incorporated into a circular production system using organic side streams.
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\n
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4. The rise of insect farming
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Until the end of the twentieth century, the most common way to collect insects worldwide was by wild harvest (circa 90%), and the tradition of collecting and eating insects from the wild is seen in many cultures. Though seasonality limits consistent availability, traditional regulation patterns can mitigate this and maintain locally sustainable sources [24, 25]. Wild catch is declining in many areas with many factors contributing to this including land conversion, overexploitation and urbanisation [7]. With insects acknowledged to be key to the delivery of many ecosystem services, their conservation in natural ecosystems is now paramount [26, 27]. In response, the farming of edible insects is now rising from being only a minor component of the market and should be promoted to improve quality and supply as well as to limit the environmental impacts of wild harvesting [11, 28].
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No matter the scale of insect farming, the economic benefits boost food security in terms of availability and accessibility and at the same time improve dietary quality and contribute to both gender equity and livelihoods. At the community scale, more than 20,000 small farmers in Thailand profitably produce crickets; in Laos, the majority of insect vendors are illiterate females who may earn c$5/day; in Uganda and Kenya, the Flying Food Project supports expansion of small-scale farms into local and greater value chain markets [20, 29, 30]. By integrating mini-livestock farming into current agricultural systems, the access to edible insects could be improved and simultaneously provide co-benefits such as female employment and a high-grade compost contribution to the enhancement of soil fertility [28]. Harvesting insects as a by-product of another industry also has substantial potential but needs more widespread implementation and cultural assimilation. For example, domesticated silkworms for the textile industry can be eaten in the pupa stage, and palm weevils reared on felled palm trees could be moved into more formal production [15]. Insect farming is now moving into western markets and developing technologically refined production systems. The French company Ynsect has raised $175 M for expansion, and the USA edible insect market is predicted to increase by 43% in the coming 5 years [31, 32]. There are different costs and benefits at all scales (Figure 1), though all may have an important place in future food security.
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Figure 1.
Trade-offs in the scale of production needed to maximise food sovereignty relative to the technology and initial funding needed. X axis: 0 = none needed, 1 = high setup costs needed. Y axis: 0 = no food sovereignty, 1 = complete food sovereignty.
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\n
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5. The environmental advantage of insect farming
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In general, insects have a lower consumption of energy and resources than conventional animal livestock. Insects are poikilothermic, so they expend less energy, are more efficient in transforming phytomass into zoomass and have higher fecundity and growth rates and a higher rate of matter assimilation. On average, an insect only needs 2 g of food per gramme of weight gained, whereas a cow needs 8 g of food. Not only is the efficiency of insect production higher because of the feed conversion ratio (Table 1) but also because the edible portion of insects is higher as crickets can be eaten whole, but we only eat 40% of a cow, 58% of a chicken and 55% of a pig [8, 10, 33].
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
Cricket
\n
Poultry
\n
Pork
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Beef
\n
\n\n\n
\n
Feed conversion ratio (kg feed: kg live weight)
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1.7
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2.5
\n
5
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10
\n
\n
\n
Edible portion (%)
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80
\n
55
\n
55
\n
40
\n
\n
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Feed (kg: kg edible weight)
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2.1
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4.5
\n
9.1
\n
25
\n
\n\n
Table 1.
Efficiencies of production of conventional meat and crickets [17].
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Edible insects are an environmentally attractive alternative to conventional livestock because they require less feed and water; they produce lower levels of greenhouse gases and can be raised in small spaces. Worldwide, livestock contributes to 18% of greenhouse gas emissions, which, in light of global warming and climate change, favours the less resource-intensive insect production which emits fewer greenhouse gases by a factor of 100 [3, 28].
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Insects can be a renewable food source in the future as many edible species can consume agricultural and food waste or culinary by-products, but there remain important research gaps in understanding the effects of variable feedstocks as most case studies use high-grade feed [10, 15, 28]. Such organic side streams could be used to reduce the environmental impact of insect farming while simultaneously creating a novel, circular waste-processing income. Throughout the world, 1/3 of all food is wasted, and household food waste is 70% of the post-farm total. If food waste was its own country, it would be the third largest emitter of greenhouse gases after the USA and China [30]. Food waste is expected to increase in the future with a continually growing and increasingly urbanised global population adopting ‘modern’ lifestyles.
\n
It is challenging and wasteful to commercialise traditional composting of multiple waste streams on a large scale, but waste can be fed directly to insects to convert low-value biomass into higher-value insect mass. By valorising waste as feed, it may mitigate the impact of the food industry. Some fly (Diptera) species are known to be able to convert agricultural manure into body mass and reduce the waste dry matter by 58%. For food waste the conversion is as high as 95% leaving the remainder as a high-grade soil improver [30, 33].
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6. Acceptability of eating insects as animal protein
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The feasibility of promoting edible insect farming as sustainable protein depends on social acceptance, as the benefits cannot be realised if people do not choose to eat insects. The understanding of current perceptions, which often depend on class, location, gender and age, is essential to any market development. In some locations, newly urbanised citizens view insects as pests or as poor person’s food [7]. Although in this particular case, acceptance does depend on the insect itself, as there is an inferiority complex associated with wild harvesting of insects. In the Western world, insects are largely unfamiliar and mostly viewed as holiday novelty or ‘yuk’; thus, awareness of local taboos, cultural preferences and the population’s exposure to insects as food are crucial for the successful promotion of insect farming for food [3, 15, 34].
\n
In many urban and developed populations, a central issue is food neophobia, but after taking the first step in trying an insect, continued exposure correlates with increased acceptance. Processed insect products such as cookies, snack bars or powders further normalise the protein source [34, 35]. Conventional meat has a special status in society, both culturally and structurally in meals, so a sustainable culinary culture must be promoted in order to associate insect protein with pleasurable food [17].
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There are also risk considerations with the dissemination of novel foods and novel production pathways. Possible effects of prolonged insect consumption are nutrient malabsorption, growth alteration, allergy risk and contamination, and more research is needed into the digestion and absorption of insects in the human body [36]. Intensive insect farming runs risks of microbial infestation, parasites and pesticides. Preventative approaches, such as probiotics, transgenerational immune priming or heat treatment, and measured responses such as those advocated by Integrated Crop Management (ICM) will develop with the industry [20, 37]. There are other limitations in the lack of protocols in storage and decontamination, and although international regulation is underway, these ancient foods are currently classified the EU as novel foods [38].
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\n
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7. Conclusion
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The issue of food security is multi-faceted, and each country’s solution will be different. Tackling food security requires responses that are both innovative and culturally appropriate. Farming insect livestock has the potential to alleviate food insecurity while promoting food sovereignty, especially if it is integrated with social acceptability in mind. Engagement of all stakeholders on the production and consumption sides and continued support for and from them will be vital for the success of its implementation. Commercial farming is growing across Europe and the North American continent, though a question yet to be answered at a wider scale is how edible insect farming can be increased and deployed in a way that benefits all parties, including especially the most vulnerable. We have overviewed the field and hope that this synthesis of much important work along with the exemplar production model of Figure 2 can provide encouragement and compact information to those seeking to evaluate the future of farmed insect production.
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Figure 2.
Idealised schematic of the inputs and outputs of a sustainable production model for insect farming.
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There is currently too little research available on the integration of insect farming with existing agricultural systems, and future solutions require the coordination of international, national and legal frameworks. With this in place, the future food revolution will be more able to directly benefit the poor and be environmentally sustainable [39].
\n
\n
Acknowledgments
\n
The authors wish to thank Harry McDade who contributed to the discussions on this topic. Thanks also go to the many who have written so passionately on this topic and to the inspiring Arnold van Huis; may these efforts eventually bear fruit, or larvae. Particular thoughts go to Dr. Marianne Schockley of the University of Georgia, Athens, GA, who advocated so ably and enthusiastically for Entomophagy in the USA.
\n
\n
Conflict of interest
\n
The authors declare no conflict of interest.
\n
\n',keywords:"climate change, sustainability, entomophagy, insectivory, acceptance",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/68137.pdf",chapterXML:"https://mts.intechopen.com/source/xml/68137.xml",downloadPdfUrl:"/chapter/pdf-download/68137",previewPdfUrl:"/chapter/pdf-preview/68137",totalDownloads:685,totalViews:0,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:1,dateSubmitted:"April 5th 2019",dateReviewed:"June 17th 2019",datePrePublished:"August 14th 2019",datePublished:"January 29th 2020",dateFinished:null,readingETA:"0",abstract:"Insect protein production through ‘mini-livestock farming’ has enormous potential to reduce the level of undernutrition in critical areas across the world. Sustainable insect farming could contribute substantially to increased food security, most especially in areas susceptible to environmental stochasticity. Entomophagy has long been acknowledged as an underutilised strategy to address issues of food security. This chapter reviews and provides a synthesis of the literature surrounding the potential of insect farming to alleviate food security while promoting food sovereignty and integrating social acceptability. These are immediate and current problems of food security and nutrition that must be solved to meet the UNDP Sustainable Development Goals.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/68137",risUrl:"/chapter/ris/68137",book:{slug:"edible-insects"},signatures:"Flora Dickie, Monami Miyamoto and C. Matilda (Tilly) Collins",authors:[{id:"301019",title:"Dr.",name:"C M",middleName:"(Tilly)",surname:"Collins",fullName:"C M Collins",slug:"c-m-collins",email:"t.collins@imperial.ac.uk",position:null,institution:null},{id:"301022",title:"Ms.",name:"Flora",middleName:null,surname:"Dickie",fullName:"Flora Dickie",slug:"flora-dickie",email:"flora.dickie17@imperial.ac.uk",position:null,institution:{name:"Imperial College London",institutionURL:null,country:{name:"United Kingdom"}}},{id:"308040",title:"Ms.",name:"Monami",middleName:null,surname:"Miyamoto",fullName:"Monami Miyamoto",slug:"monami-miyamoto",email:"monami.miyamoto17@imperial.ac.uk",position:null,institution:{name:"Imperial College London",institutionURL:null,country:{name:"United Kingdom"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Food insecurity",level:"1"},{id:"sec_3",title:"3. Nutritional potential of edible insects",level:"1"},{id:"sec_4",title:"4. The rise of insect farming",level:"1"},{id:"sec_5",title:"5. The environmental advantage of insect farming",level:"1"},{id:"sec_6",title:"6. Acceptability of eating insects as animal protein",level:"1"},{id:"sec_7",title:"7. Conclusion",level:"1"},{id:"sec_8",title:"Acknowledgments",level:"1"},{id:"sec_8",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'MacEvilly C. Bugs in the system. Nutrition Bulletin. 2000;25(4):267-268'},{id:"B2",body:'Kelemu S, Niassy S, Torto B, Fiaboe K, Affognon H, Tonnang H, et al. African edible insects for food and feed: Inventory, diversity, commonalities and contribution to food security. Journal of Insects as Food and Feed. 2015;1(2):103-119'},{id:"B3",body:'Stull VJ, Wamulume M, Mwalukanga MI, Banda A, Bergmans RS, Bell MM. “We like insects here”: Entomophagy and society in a Zambian village. Agriculture and Human Values. 2018;35(4):867-883'},{id:"B4",body:'Yhoung-Aree J, Viwatpanich K. Edible insects in the Lao PDR, Myanmar, Thailand and Vietnam. In: Paoletti MG, editor. Ecological implications of minilivestock: Potential of insects, rodents, frogs and snails. Enfield, NH, USA: Science Publisher Inc; 2005. pp. 415-440'},{id:"B5",body:'Ramos-Elorduy J. Anthropo-entomophagy: Cultures, evolution and sustainability. Entomological Research. 2009;39:271-288'},{id:"B6",body:'Belluco S, Losasso C, Maggioletti M, Alonzi CC, Paoletti MG, Ricci A. Edible insects in a food safety and nutritional perspective: A critical review. Comprehensive Reviews in Food Science and Food Safety. 2013;12(3):296-313'},{id:"B7",body:'Looy H, Dunkel FV, Wood JR. How then shall we eat? Insect-eating attitudes and sustainable foodways. Agriculture and Human Values. 2014;31(1):131-141'},{id:"B8",body:'Vogel G. For more protein, filet of cricket. Science. 2010;327(5967):881'},{id:"B9",body:'Tomberlin JK, Zheng L, van Huis A. Insects to feed the world conference 2018. 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Environmental Health Perspectives. 2011;119(12):1817-1823'},{id:"B15",body:'Laar A, Kotoh A, Parker M, Milani P, Tawiah C, Soor S, et al. An exploration of edible palm weevil larvae (Akokono) as a source of nutrition and livelihood: Perspectives from Ghanaian stakeholders. Food and Nutrition Bulletin. 2017;38(4):455-467'},{id:"B16",body:'van Huis A. Potential of insects as food and feed in assuring food security. Annual Review of Entomology. 2013;58(1):563-583'},{id:"B17",body:'van der Spiegel M, Noordam MY, van der Fels-Klerx HJ. Safety of novel protein sources (insects, microalgae, seaweed, duckweed, and rapeseed) and legislative aspects for their application in food and feed production. Comprehensive Reviews in Food Science and Food Safety. 2013;12:662-678'},{id:"B18",body:'Oonincx DGAB, de Boer IJM. Environmental impact of the production of mealworms as a protein source for humans: A life cycle assessment. PLoS ONE. 2012;7:12'},{id:"B19",body:'Ozimek L, Sauer WC, Kozikowski V, Ryan JK, Jørgensen H, Jelen P. Nutritive value of protein extracted from honey bees. Journal of Food Science. 1985;50(5):1327-1329'},{id:"B20",body:'Barennes H, Phimmasane M, Rajaonarivo C. Insect consumption to address undernutrition, a national survey on the prevalence of insect consumption among adults and vendors in Laos. PLoS ONE. 2015;10(8)'},{id:"B21",body:'Payne CLR, Scarborough P, Rayner M, Nonaka K. Are edible insects more or less “healthy” than commonly consumed meats? A comparison using two nutrient profiling models developed to combat over- and undernutrition. European Journal of Clinical Nutrition. 2016;70(3):285-291'},{id:"B22",body:'van Huis A, Oonincx DGAB. The environmental sustainability of insects as food and feed: A review. Agronomy for Sustainable Development. 2017;35(7):1-14'},{id:"B23",body:'Banjo A, Lawal O, Sononga E. The nutritional value of fourteen species of edible insects in southwestern Nigeria. African Journal of Biotechnology. 2006;5:298-301'},{id:"B24",body:'Illgner P, Nel E. The geography of edible insects in sub-Saharan Africa: A study of the mopane caterpillar. The Geographical Journal. 2000;166(4):336-351'},{id:"B25",body:'Mbata KJ, Chidumayo EN, Lwatula CM. Traditional regulation of edible caterpillar exploitation in the Kopa area of Mpika district in northern Zambia. Journal of Insect Conservation. 2002;6(115)'},{id:"B26",body:'Losey JE, Vaughn M. The economic value of ecological services provided by insects. Bioscience. 2006;56(4):311'},{id:"B27",body:'Sánchez-Bayo F, Wyckhuys KAG. Worldwide decline of the entomofauna: A review of its drivers. Biological Conservation. 2019;232:8-27'},{id:"B28",body:'Nadeau L, Nadeau I, Franklin F, Dunkel F. The potential for entomophagy to address undernutrition. Ecology of Food and Nutrition. 2015;54(3):200-208'},{id:"B29",body:'Halloran A, Vantomme P, Hanboonsong Y, Ekesi S. Regulating edible insects: The challenge of addressing food security, nature conservation, and the erosion of traditional food culture. Food Security. 2015;7(3):739-746'},{id:"B30",body:'Entomics. Entomics [Internet]. Available from: www.entomics.com'},{id:"B31",body:'Ynsect [Internet]. 2019. Available from: http://www.ynsect.com/en/'},{id:"B32",body:'Ahuja K, Deb S. Edible insects: Market size by product, by application, industry analysis report, regional outlook, application potential, price trends, competitive market share and forecast, 2018-2024. Delaware, USA: Global Market Insights; 2018'},{id:"B33",body:'van Huis A, Klunder JVIH, Merten E, Halloran A, Vantomme P. Edible Insects. Future Prospects for Food and Feed Security. Rome: Food and Agriculture Organization of the United Nations; 2013'},{id:"B34",body:'Collins CM, Vaskou P, Kountouris Y. Insect food products in the Western world: Assessing the potential of a new ‘green’ market. Annals of the Entomological Society of America. 2019. IN PRESS'},{id:"B35",body:'Hartmann C, Siegrist M. Becoming an insectivore: Results of an experiment. Food Quality and Preference. 2016;51:118-122'},{id:"B36",body:'Testa M, Stillo M, Maffei G, Andriolo V, Gardois P, Zotti CM. Ugly but tasty: A systematic review of possible human and animal health risks related to entomophagy. Critical Reviews in Food Science and Nutrition. 2017'},{id:"B37",body:'Grau T, Vilcinskas A, Joop G. Sustainable farming of the mealworm Tenebrio molitor for the production of food and feed. Zeitschrift fur Naturforschung: Section C Journal of Biosciences. 2017;72(9):337-349'},{id:"B38",body:'Finke MD, Rojo S, Roos N, van Huis A, Yen AL. The European food safety authority scientific opinion on a risk profile related to production and consumption of insects as food and feed. Journal of Insects as Food and Feed. 2015;1(4):245-247'},{id:"B39",body:'Conway G, Wilson K. One Billion Hungry. 1st Editio ed. Ithaca, N.Y.: Comstock Publ. Assoc; 2012'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Flora Dickie",address:null,affiliation:'
Department of Life Sciences, Imperial College London, United Kingdom
Centre for Environmental Policy, Imperial College London, United Kingdom
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1. Introduction
The rapidly growing number of mobile devices, higher data rates and cellular traffic, and quality of service requirements trigger the development of mobile communications. It is expected that the next-generation cellular networks (5G and beyond) will meet the advanced technology requirements. 4G networks are not powerful enough to support massively connected devices with low latency and high spectral efficiency, which is critical for next-generation networks. 5G networks are characterized by three fundamental functions in general: connectivity for everywhere, low latency for communication, and very high-speed data transmission [1].
In the near future, a large number of mobile devices will connect to one another in everywhere and provide a seamless mobile user experience. Real-time applications and critical systems and services (medical applications, traffic flow, etc.) with zero latency are expected to be offered over 5G cellular networks. Besides, the fast data transmission and reception will be ensured by supporting zero latency using a high-speed link. For this reason, the scope of 5G cellular networks bring the emerging advantages, new architectures, methodologies, and technologies on telecommunications such as energy-efficient heterogeneous networks, software-defined networks (SDN), full-duplex radio communications, device-to-device (D2D) communications, and cognitive radio (CR) networks. An increasing number of mobile devices and the bandwidth requirement for large amounts of data require the development of the new technologies and infrastructures in addition to the existing technology. It is inevitable that the number of smart phones, high-definition televisions, cameras, computers, transport systems, video surveillance systems, robots, sensors, and wearable devices produces a huge amount of voice-data traffic in the near future. To meet the growth and to provide fast and ubiquitous Internet access, several promising technologies have been developed. Regarding the deployment of the 5G wireless communication systems, the corresponding growth in the demand for wireless radio spectrum resources will appear. The capacity of the communication networks will be increased by using the energy-efficiency techniques with the evolving technology in 5G networks [2, 3, 4, 5].
One of the candidates for solving the problem of spectrum shortage is the CR network which will be a key technology for 5G networks. CR has attracted considerable interest as it can cope with the spectrum underutilization phenomenon. Performing spectrum sharing using a CR network is an important issue in wireless communication networks. There are three main ways for a primary network user to share the frequency spectrum with a cognitive user: underlay, overlay, and interweave. In the underlay method, the secondary user (SU) transmits its information simultaneously with the primary user (PU) as long as the interference between SU and PU receivers is within a predefined threshold. In the overlay approach, SU helps PU by sharing its resources, and in return, PU allows SU to communicate. In the interweave technique, SU can use the bandwidth of PU if PU is not active. In this model, SU should have perfect spectrum-sensing features to analyze the spectrum [6, 7, 8, 9].
Among the various methods of solving the interference problem, interference alignment (IA) is one of the most promising ways to achieve it. IA is an important approach for CR to recover the desired signal by utilizing the precoding and linear suppression matrices which consolidates the interference beam into one subspace in order to eliminate it [10, 11, 12, 13]. In the literature, linear IA is adopted in CR interference channels in [14, 15, 16, 17, 18, 19, 20] and the references therein. In [14], adaptive power allocation schemes are considered for linear IA-based CR networks where the outage probability and sum rate were derived. In [15], adaptive power allocation was studied for linear IA-based CR using antenna selection at the receiver side. Ref. [16] enhances the security of CR networks by using a zero-forcing precoder. Moreover, in [17], a similar work was proposed to improve the overall outage performance of the interference channel by using power allocation optimization. These studies have shown that interference management is a critical issue to be handled in all multiuser wireless networks.
CR technology can be capable of utilizing the spectrum efficiently as long as the interference between PU and SU is perfectly aligned as shown in Figure 1. A set of studies discussing IA is presented in the literature [21, 22, 23, 24, 25, 26, 27, 28, 29].
Figure 1.
Illustration of the primary link between PU pair and interference links generated by the SUs.
Motivated by the above works, in the first part of this study, we examine the impact of interference leakage on multi-input multi-output (MIMO) CR networks with multiple SUs. Specifically, a closed-form outage probability expression is derived to provide the performance of the primary system. Then, in the second part of our work, we investigate the performance of IA in underlay CR networks for Rayleigh fading channel. Moreover, unlike the mentioned papers, the effect of CSI quantization error is taken into account in our analysis. Then, a two-way relaying scheme with amplify-and-forward (AF) strategy is studied. Finally, the effects of the relay location and the path loss exponent on the BER performance and system capacity and CSI quantization on the average pairwise error probability (PEP) performance for this two-way AF system are presented.
The main simulation parameters and their descriptions used in this study are summarized in Table 1.
Symbol
Description
P1 and P2
Transmitted powers of the PU and SU
σN2
Variance of the circularly symmetric additive white Gaussian noise vector
Rth
Data rate threshold
α
Interference-leakage parameter
Mp and Np
Number of transmit-and-receive antennas of PU
Ms and Ns
Number of transmit-and-receive antennas of SU
K
Number of SU
dj,i
Distance between the i th transmitter and the j th receiver nodes
τj,i
Path loss exponent between the i th transmitter and the j th receiver nodes
Bj,i
Channel state information exchange amount between the i th transmitter and the j th receiver nodes
Table 1.
The simulation symbols and their descriptions.
2. The impact of interference leakage on MIMO CR networks
In this study, MIMO interference alignment-based CR network with a PU and multiple SUs is considered under Rayleigh fading channel.
2.1. System model
In the system model as it is shown in Figure 2, the number of transmit-and-receive antennas of the PU is given by Mp and Np. The transmit antennas at each SU are given as Ms. The received signal, yp, implementing the IA technique is given as
yp=UpHHppVpxp+α∑i=1KUsHHpsiVsxsi+UpHn,E1
where xp and xsi are the transmitted signals from PU and the i th SU fori=12…K, respectively. Herein, Hpp is the matrix of channel coefficients between the PU pair, and Hpsi denotes the channel matrix between the primary receiver and the i th secondary transmitter. The interference leakage is modeled similar to the one in [30]. The interference-leakage parameter α0≤α≤1 represents the status of the alignment, i.e., α=0and1 corresponds to perfect alignment and perfect misalignment cases, respectively. V and U are the precoding- and interference-suppression matrices. The superscript ⋅H denotes the Hermitian operator, and n is the zero-mean unit variance (σN2=1) circularly symmetric additive white Gaussian noise (AWGN) vector.
Figure 2.
IA-based CR network with single PU and K SUs sharing the spectrum.
The following conditions must be satisfied for perfect interference alignment between PU and SUs:
UsHHpsiVsxsi=0,E2
RankUsHHpsiVsxsi=d.E3
Each user transmits d data streams. Using the ideal linear IA technique, (1) can be re-expressed as
yp=UpHHppVpxp+UpHn.E4
2.2. Outage probability analysis
The channel capacity and outage probability are the most important impairments which affect the quality of service (QoS) in wireless communication systems. When no CSI conditions are given, MIMO channel capacity is expressed as in [31]. The channel capacity of the considered MIMO system in PU can be expressed as
C=log2detI+γ11+γ2NpHppHppH,E5
where γ1=P1Hpp2/σN2 is the signal-to-noise ratio (SNR) of the primary link. γ2 can be expressed as γ2=P2/σN2∑i=1KHpsi2. Note that .2 demonstrates the squared Frobenius norm of the channel matrix, I denotes for identity matrix, and P1 and P2 are the transmitted powers of the PU and SUs, respectively. If linear IA perfectly eliminates the interference between SU and PU, then SNR of the interference channel, γ2, becomes zero. It is important to note that precoding and linear suppression vectors are assumed as UpH2=Vp2=UsiH2=Vsi2=1. In the presence of interference-free communication, primary system works in the single-input and single-output (SISO) fashion [14]. Hence, the probability density function (PDF) of γ1 can be written as fγ1γ=1γ¯1exp−γ/γ¯1, and the outage probability of the system can be obtained as
Pout=∫02Rth−1fγ1γdγ,E6
where Rth is the data rate threshold and γ¯1=P1/σN2 denotes the average SNR of the primary system. By substituting fγ1γ into (6), the outage probability can be obtained as
Pout=1−exp2Rth−1γ¯1.E7
In the presence of interference, the primary system works in MIMO fashion, and leakages may occur due to fast-fading Rayleigh channel. To improve the performance of the primary system, we adopt maximum ratio transmission and maximum ratio combining at the transmitter and receiver, respectively. Thereby, the end-to-end signal-to-interference-plus-noise ratio (SINR) of the primary system can be written as γτ=γ1/1+γ2. In the proposed system, all channels are modeled as independent and identically distributed Chi-squared distribution, and the PDF of γ1 can be expressed as
fγ1γ=γMpNp−1exp−γ/γ¯1/Mpγ¯1MpMpNpMpNp−1!.E8
In addition, the PDF of γ2 can be defined as
fγ2γ=γKMsNp−1exp−γ/αγ¯2/Msαγ¯2MsKMsNpKMsNp−1!,E9
where γ¯2=P2/σN2 is the average SNR of the secondary system. Finally, the PDF of γτ can be written as
fγτγ=∫0∞x+1fγ1x+1γfγ2xdx.E10
By substituting (8) and (9) into (10), then with the help of [32, Eq. 3.351.3] and after few manipulations, PDF expression of fγτγ is given as
Furthermore, collecting constant terms in (11), Δ is defined by
Δ=βγMpNp−1exp−Mpγγ¯1.E12
Hereby, β is constituted as
β=γ¯1Mp−MpNpαγ¯2Ms−KMsNpMpNp−1!KMsNp−1!.E13
To achieve the closed-form expression of (11), binomial expression of γMpγ¯1+Msαγ¯2−KMsNp+m term must be completed. The binomial expansion of this negative exponential term is given as
where ζ is given as ζ=KMsNp+m. Besides, the validation of (14) is restricted via ∣γMpγ¯1∣<Msαγ¯2 condition. Under these conditions, the closed-form expression of fγτ is given below:
Outage probability function of the proposed MIMO system with respect to fγτ can be expressed as
Pout=∫02Rth−1fγτγdγ.E16
The closed-form expression for (16) can be validated with the numerical integral operation [33].
2.3. Performance evaluation
Herein, the system performance of the MIMO CR network is studied in the presence of interference leakage for Rayleigh fading channel by comparing the analytical results with computer simulations. We assumed P1 = P2 = ρ while σN2=1 in the performance evaluation.
In Figure 3, the Pout performance for different Rth values is presented. We take α=−20 dB, Mp=2, Np=2, K=5, and Ms=1. It can be seen from Figure 3 that when Rth is increased from 1 to 4 bits/channel, the Pout performance is degraded.
Figure 3.
Pout performance for different data rate threshold Rth.
In Figure 4, the impact of the leakage coefficient, α, on the outage probability performance is depicted for Mp=2, Np=2, K=1, Ms=1, and Rth=3 bits/channel. As can be seen from the figure, when α is changed from −10 dB to −30 dB, the performance of the primary system is enhanced.
Figure 4.
Pout performance with varying SNR for different interference-leakage values.
In Figure 5, α, Mp, Np, Ms, and Rth are taken as −20 dB, 2, 2, 1, and 1 bits/channel, respectively. It can be observed from the figure that increasing the number of SUs decreases the outage probability performance of the primary system considerably.
Figure 5.
Pout vs. SNR for different numbers of SUs.
In Figure 6, the impact of antenna diversity on the Pout performance is investigated for α=−10 dB, K=2, and Rth=1 dB. It is observed from the figure that, when the number of antennas at the primary transmitter and receiver increases, the system performance enhances. Besides, the receiver diversity effect on the system performance is greater than the transmitter diversity, as expected.
Figure 6.
The effect of antenna diversity on the outage probability performance.
3. The effect of CSI quantization on interference alignment in CR networks
In this section, we investigate a cognitive two-way relaying network composed of a primary network (PN) with one pair of PU and a secondary network (SN) with two source terminals and a relay terminal (R).
3.1. System model
We consider a MIMO interference network shown in Figure 7, where the transmitter, Tx, and receiver, Rx, are equipped with M1 and N1 antennas in PN, respectively. Each PN transmitter transmits to its corresponding receiver by interfering with the SN nodes, namely, two source terminals (S1 and S2) and a relay terminal. That means Tx transmitter sends messages to its intended receiver Rx, whereas it also causes interference to the unintended receivers in the SN. The SN consists of two source terminals and a relay terminal. We assume that all nodes in SN operate in an AF half-duplex mode with the help of information relaying from each source terminal to R in two phases. All nodes in SN are assumed to have MIMO antennas, and there is no direct transmission between S1 and S2 [34, 35, 36]. We consider a scenario where the source terminals and a relay terminal are equipped with NS1, NS2, and NR antennas, respectively. In the system model based on IA for cognitive two-way relay network, the received signal at Rx in PN can be written as
yRx=PTxdRx,TxτRx,TxURxHHRx,TxVTxsTx+ϒ+n˜Rx,E17
where ϒ is the interference term generated from SN to Rx defined as follows:
System model for interference alignment-based cognitive two-way relay network with primary network and secondary network.
The effective additive white Gaussian noise (AWGN) term with zero mean and unit variance, n˜Rx at Rx in PN, is defined by URxHnRx, where nRx is the AWGN vector with E nRxnRxH = σRx2I in which I is the unitary matrix, σRx2 is the noise variance, and E . is the expectation operator. The transmit powers at the terminals Tx, S1, S2, and R are denoted by Pi, for i = Tx, S1, S2, and R, respectively. Each receive node employs the interference-suppression matrix, Uj, (for j = Rx, R, S1, S2), while each transmit node employs a precoding matrix Vi [37]. The conjugate transpose of the matrix is associated with the Hermitian operator .H [38]. The transmit signal vector for the i th user is defined by si. The channel between the i th transmitter and the j th receiver nodes is denoted by Hj,i for both PN and SN. The quantized CSI is passed to the transmitter by the corresponding receiver. Because of limited feedback, the transmitters have imperfect CSI causing certain performance loss. To clarify the effect of CSI quantization error on the performance of interference alignment in underlay cognitive two-way relay networks, we investigate the BER performance, instantaneous capacity, and average PEP of the considered system. Based upon the accuracy parameter, the relation between perfect CSI (ρj,i=0) and imperfect CSI (0<ρj,i≤1) can be given as
Hj,i=1−ρj,iĤj,i+ρj,iEj,i,E19
where Hj,i is the real channel matrix and Ĥj,i is the estimated channel matrix. The quantization error, Ej,i1mm, can be expressed with the upper bound of 2−Bj,i/M1N1−1, where Bj,i is the CSI exchange amount and M1 and N1 are the numbers of transmit-and-receive antennas, successively [21, 39]. It is assumed that both Ĥj,i and Ej,i are independent of Hj,i. Besides, each channel link is also modeled by two additional parameters: the distance between i th transmitter and the j th receiver nodes dj,i and the path loss exponent for the corresponding link, τj,i, regarding for different radio environments, respectively.
In the first phase of the transmission (multiple-access phase) in SN, both S1 and S2 transmit their signals simultaneously to the relay terminal, R. Then the received signal at R can be written as
where n˜R=URHnR at the relay terminal in SN is expressed as zero-mean AWGN vector with E nRnRH = σR2I in which the noise variance at the relay terminal is depicted with σR2. Besides, the received signal at S1 and S2 terminals in SN is defined, respectively, as
Here, n˜S1 and n˜S2 are the AWGN vector with E nSknSkH = σSk2I, for k=1,2 and the noise variance of σSk2. In addition to that, in the second phase of the signal transmission (broadcast phase), R broadcasts the combined signal yR after multiplying with an ideal amplifying gain, G, which is expressed as
where sR=GyR. We assume that both S1 and S2 have knowledge about their own information and can remove back-propagating self-interference from the imposed signals. We also assume that all interference at the receive terminals are perfectly aligned and the following feasible conditions are satisfied for the receive nodes:
UjHHj,iVisi=0,E24
rankUjHHj,iVisi=fi,E25
where fi is the degree of freedom and rank (.) denotes the rank operation of a matrix. By assuming that the interference is perfectly aligned by the proposed IA algorithm, and the channel matrices are constant during the transmission, we ensure that there is no interference from the unintended transmitters and guarantee that received signal achieves fi degrees of freedom [39]. The corresponding signal-to-interference-plus-noise ratio (SINR) for the links Tx→Rx, S1→R, and R→S2 can be derived by
where Ej,i is the quantization error and . is the Euclidean norm. In here, γS2→R and γR→S1 can be found by changing the subscript S1 with S2 of (27) and S2 with S1 of (28). Assuming the channels are reciprocal over SN direct links, thus the channel gains for S1→R and R→S1 and S2→R and R→S2 links are identical, respectively.
3.2. Performance analysis
This section starts by the instantaneous capacity analysis of the proposed system with interference alignment in underlay cognitive two-way relay networks with CSI quantization. We then study the BER and average PEP performance.
The capacity is expressed as the expected value of the mutual information between the transmitting terminal and receiving one. In light of this fact, we consider the method developed in [29]; the instantaneous capacity in PN can be expressed as
CRx=log21+γTx→Rx,E30
where γTx→Rx is the instantaneous SINR for the corresponding link of Tx→Rx. On the other hand, end-to-end capacity for the SN, based on the least strong link over two-hop transmission, is denoted as follows:
γS1→R and γR→S2 are the instantaneous SINR for the S1→R and R→S2 links, respectively.
Average BER for binary phase shift keying (BPSK) modulation can be expressed as
BERj=QγjE32
where Qx is the Gaussian Q-function and defined by Qx=1/2π∫x∞e−t2/2dt [37].
Average pairwise error probability (PEP¯) can be computed as averaging the Gaussian Q-function over Rayleigh fading statistics [40], fγTx→Rxγ=e−γ/γ¯Tx→Rx/γ¯Tx→Rx1mm, where γ¯Tx→Rx=PTx1−ρRx,Tx/dRx,TxτRx,Txσn˜Rx2
PEP¯=∫0∞QγTx→RxfγTx→Rxγdγ.E33
Finally, this integral can be evaluated with the help of Mathematica and average PEP under Rayleigh fading channel can be derived in a closed form as follows:
PEP¯=121−γ¯Tx→Rx2+γ¯Tx→Rx.E34
3.3. Numerical results
In this section the numerical results are provided with various scenarios to evaluate the performance analysis for IA in underlay cognitive two-way relay networks with CSI quantization. BER performance for direct transmission links of the proposed system is illustrated in Figure 8 over Rayleigh distribution for different amounts of CSI exchange with varying SNR. For convenience, we set dj,i=3m and τ=2.7, and 3 × 3 MIMO configuration is studied in this figure. Because of the number of interfering links, the quantization error for the Tx→Rx transmission is greater than the other links (S1⇄R⇄S2). Even if the analyzed BER performance of the SN seems better than the PN, it should not be forgotten that SN operates in half-duplex mode. Performance loss in BER due to imperfect CSI (Bj,i=4, for instance) becomes larger as SNR increases compared to the perfect CSI (for Bj,i=∞) case.
Figure 8.
BER performance for different amounts of CSI exchange with varying SNR.
In Figure 9, the average PEP versus SNR is plotted for dj,i=3m and τ=2.7 over Rayleigh fading channel in PN. It can be noticed from the figure that as SNR increases, average PEP decreases, as expected. To reach the perfect CSI case, we take Bj,i=∞, and the average PEP performance noticeably enhances. We also consider the case of imperfect CSI (Bj,i=4) for the comparison purposes in the same figure.
Figure 9.
Average PEP performance for different amounts of CSI exchange with varying SNR over Rayleigh fading channel in primary network.
Figure 10 examines the capacity analysis with perfect and imperfect CSI for different direct links in PN and SN. The results clearly show that, examining the capacity with perfect CSI, performance improvement becomes larger as the SNR increases.
Figure 10.
Capacity vs. SNR of the primary network and secondary network nodes under different CSI scenarios.
Figure 11 demonstrates the effects of Bj,i and dj,i parameters on the BER performance for the SN with varying SNR when τ=2.7 and 3 × 3 MIMO scheme is used. The results clearly show that for a fixed SNR value, the performance of the considered system increases with the decrease of the dj,i. It can be seen from the same figure that the increase on the amount of CSI exchange Bj,i positively affects the BER performance.
Figure 11.
BER performance for different amounts of CSI exchange and distances with varying SNR over Rayleigh fading channel for secondary network.
Figure 12 shows the capacity performance of PU in the underlay cognitive two-way relay network over Rayleigh fading channel with varying path loss exponent, τ. The results show a performance improvement while the value of τ decreases. In this plot, Bj,i = 8, dj,i = 3m, and the 3 × 3 MIMO scheme are considered. Depending on the environmental conditions for mobile communications, typical τ values, ranging from 1.6 to 5, are used to plot this figure. First, for the line of sight in a building, the environment is considered with the τ values of 1.6 and 1.8. Second, capacity is computed for the free-space environment with τ=2. Then, the capacity performance is presented with τ values of 2.7 and 3.3 for urban area cellular radio environment. Finally, the shadowed urban cellular radio environment is associated with two different τ values of 3 and 5 to analyze the capacity performance with varying SNR [41].
Figure 12.
Capacity changes with SNR for the environmental conditions having different path loss exponents.
4. Conclusion
In this chapter, the system performance of linear interference alignment on the MIMO CR network is investigated under interference leakage. To quantify the performance of the primary system under a certain level of interference leakage, the closed-form outage probability expression is derived for Rayleigh fading channel. In all analyses, the theoretical results closely match with the simulations which confirm the accuracy of the derived expressions.
In the second part of this work, considering a practical issue, we investigate the performance of interference alignment in underlay cognitive radio network with CSI quantization error over general MIMO interference channel. Amplify-and-forward scheme for two-way relay network under Rayleigh fading is considered. The impact of the CSI exchange amount, the distance between the i th transmitter and the j th receiver nodes, and the path loss exponent on the BER performance, system capacity, and average PEP for the proposed system model are analyzed. We provide the exact closed-form expression for the average PEP in primary network over Rayleigh distribution, while IA algorithm perfectly eliminates the interference. The present performance analysis can be extended to the multiple secondary user pairs, and this approach will be another subject of our future work.
It would be interesting to study on various scenarios, including single-hop, multi-hop, and multi-way networks in future work to analyze the system performance over the recently developed interference alignment algorithms for next-generation 5G wireless communication systems.
Acknowledgments
The authors wish to express their special thanks to Seda Ustunbas (Wireless Communication Research Laboratory, Istanbul Technical University, Turkey) for useful discussions of this chapter.
\n',keywords:"5G wireless communication systems, average pairwise error probability, CSI quantization, cognitive radio networks, interference alignment, MIMO, outage probability performance, two-way amplify-and-forward relaying",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/63431.pdf",chapterXML:"https://mts.intechopen.com/source/xml/63431.xml",downloadPdfUrl:"/chapter/pdf-download/63431",previewPdfUrl:"/chapter/pdf-preview/63431",totalDownloads:547,totalViews:254,totalCrossrefCites:1,dateSubmitted:"February 28th 2018",dateReviewed:"July 9th 2018",datePrePublished:"November 5th 2018",datePublished:"December 5th 2018",dateFinished:null,readingETA:"0",abstract:"This study investigates the interference alignment techniques for cognitive radio networks toward 5G to meet the demand and challenges for future wireless communications requirements. In this context, we examine the performance of the interference alignment in two parts. In the first part of this chapter, a multi-input multi-output (MIMO) cognitive radio network in the presence of multiple secondary users (SUs) is investigated. The proposed model assumes that linear interference alignment is used at the primary system to lessen the interference between primary and secondary networks. Herein, we derive the closed-form mathematical equations for the outage probability considering the interference leakage occurred in the primary system. The second part of this study analyzes the performance of interference alignment for underlay cognitive two-way relay networks with channel state information (CSI) quantization error. Here, a two-way amplify-and-forward relaying scheme is considered for independent and identically distributed Rayleigh fading channel. The closed-form average pairwise error probability expressions are derived, and the effect of CSI quantization error is analyzed based on the bit error rate performance. Finally, we evaluate the instantaneous capacity for both primary and secondary networks*.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/63431",risUrl:"/chapter/ris/63431",signatures:"Arif Basgumus, Mustafa Namdar, Hakan Alakoca, Eylem Erdogan\nand Lutfiye Durak-Ata",book:{id:"7291",title:"Cognitive Radio in 4G/5G Wireless Communication Systems",subtitle:null,fullTitle:"Cognitive Radio in 4G/5G Wireless Communication Systems",slug:"cognitive-radio-in-4g-5g-wireless-communication-systems",publishedDate:"December 5th 2018",bookSignature:"Shahriar Shirvani Moghaddam",coverURL:"https://cdn.intechopen.com/books/images_new/7291.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"185038",title:"Dr.",name:"Shahriar",middleName:null,surname:"Shirvani Moghaddam",slug:"shahriar-shirvani-moghaddam",fullName:"Shahriar Shirvani Moghaddam"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"19414",title:"Prof.",name:"Lutfiye",middleName:null,surname:"Durak-Ata",fullName:"Lutfiye Durak-Ata",slug:"lutfiye-durak-ata",email:"lutfiye@ieee.org",position:null,institution:{name:"Istanbul Technical University",institutionURL:null,country:{name:"Turkey"}}},{id:"179108",title:"Dr.",name:"Arif",middleName:null,surname:"Basgumus",fullName:"Arif Basgumus",slug:"arif-basgumus",email:"basgumus@gmail.com",position:null,institution:{name:"Dumlupinar University",institutionURL:null,country:{name:"Turkey"}}},{id:"180250",title:"Dr.",name:"Mustafa",middleName:null,surname:"Namdar",fullName:"Mustafa Namdar",slug:"mustafa-namdar",email:"mustafa.namdar@gmail.com",position:null,institution:{name:"Dumlupinar University",institutionURL:null,country:{name:"Turkey"}}},{id:"248380",title:"Dr.",name:"Eylem",middleName:null,surname:"Erdogan",fullName:"Eylem Erdogan",slug:"eylem-erdogan",email:"erdoganeyl@gmail.com",position:null,institution:null},{id:"248382",title:"Mr.",name:"Hakan",middleName:null,surname:"Alakoca",fullName:"Hakan Alakoca",slug:"hakan-alakoca",email:"hakan.alakoca@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. 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Amsterdam: Elsevier/Academic Press; 2007'},{id:"B33",body:'Ghavami H, Moghaddam SS. Outage probability of device to device communications underlaying cellular network in Suzuki fading channel. IEEE Communications Letters. May 2017;21(5):1203-1206'},{id:"B34",body:'Toan HV, Bao VNQ, Le KN. Performance analysis of cognitive underlay two-way relay networks with interference and imperfect channel state information. EURASIP Journal on Wireless Communications and Networking. Nov. 2018;2018(53):1-10'},{id:"B35",body:'Zhang C, Ge J, Li J, Rui Y, Guizani M. A unified approach for calculating the outage performance of two-way AF relaying over fading channels. IEEE Transactions on Vehicular Technology. Mar. 2015;64(3):1218-1229'},{id:"B36",body:'Nasir A, Zhou X, Durrani S, Kennedy R. Relaying protocols for wireless energy harvesting and information processing. IEEE Transactions on Wireless Communications. Jul. 2013;12(7):3622-3636'},{id:"B37",body:'Sung H, Park SH, Lee KJ, Lee I. Linear precoder designs for K-user interference channels. IEEE Transactions on Wireless Communications. Jan. 2010;9(1):291-301'},{id:"B38",body:'Jindal N. MIMO broadcast channels with finite-rate feedback. IEEE Transactions on Information Theory. Nov. 2006;52(11):5045-5060'},{id:"B39",body:'Zhao N, Yu FR, Sun H, Li M. Adaptive power allocation schemes for spectrum sharing in interference-alignment-based cognitive radio networks. IEEE Transactions on Vehicular Technology. May 2016;65(5):3700-3714'},{id:"B40",body:'Afana A, Mahady IA, Ikki S. Quadrature spatial modulation in MIMO cognitive radio systems with imperfect channel estimation and limited feedback. IEEE Transactions on Communications. Mar. 2017;65(3):981-991'},{id:"B41",body:'Rappaport TS. Wireless Communications: Principles and Practice. 2nd ed. New Jersey: Prentice Hall; 2001'}],footnotes:[{id:"fn1",explanation:"The content of this study has partially been submitted in IEEE 41st International Conference on Telecommunications and Signal Processing (TSP 2018)."}],contributors:[{corresp:null,contributorFullName:"Arif Basgumus",address:null,affiliation:'
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