Skip to main content
SpringerLink
Log in

Principles and Challenges of Cooperative Spectrum Sensing in Cognitive Radio Networks

  • Living reference work entry
  • First Online:
Handbook of Cognitive Radio

Abstract

Cognitive radio (CR) technology is a promising solution to the inevitable problem of spectrum scarcity and underutilization. Cognitive radios can perform spectrum sensing, dynamically identify unused spectrum, and opportunistically utilize those spectrum holes for their own transmission. Cognitive radio technology is also a key concept suggested to be part of the fifth generation of cellular wireless standards (5G). Efficient spectrum sensing is crucial to the effective deployment of CR networks. Cooperative spectrum sensing (CSS) schemes can significantly improve the sensing accuracy of CR networks by exploiting multiuser spatial diversity. However, the cooperative gain can be impacted by factors such as the detection performance of each secondary user (SU) and the fusion techniques used to combine the secondary users’ decisions. Moreover, CSS incurs cooperation overhead that may deteriorate its overall performance. In this chapter, we provide a comprehensive survey on the different factors that contribute to the efficient design of CSS schemes for cognitive radio networks. We specifically focus on the elements of cooperative sensing that can leverage the achievable cooperative gain, limit the cooperation overhead, or provide trade-off between the gain and overhead such as the number of channels sensed in each sensing period, the selection of secondary users, the selection of the fusion scheme, and the correlation between the cooperating secondary users. We also highlight key open research challenges in cooperative spectrum sensing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Mitola I, Maguire J (1999) Cognitive radio: making software radios more personal. IEEE Pers Commun Mag 6:13–18

    Article  Google Scholar 

  2. Liang Y, Chen K, Li GY, Mhnen P (2011) Cognitive radio networking and communications: an overview. IEEE Trans Veh Technol 60:3386–3407

    Article  Google Scholar 

  3. Federal Communications Commission (2002) Spectrum policy task force report. ET Docket no. 02-135

    Google Scholar 

  4. Stevenson CR, Chouinard G, Lei Z, Hu W, Shellhammer SJ, Caldwell W (2009) IEEE 802.22: the first cognitive radio wireless regional area network standard. IEEE Commun Mag 47:130–138

    Article  Google Scholar 

  5. Nekovee M (2010) Cognitive radio access to TV white spaces: spectrum opportunities, commercial applications and remaining technology challenges. In: IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Singapore, Apr 2010, pp 1–10

    Google Scholar 

  6. Ghasemi A, Sousa ES (2008) Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs. IEEE Commun Mag 46:32–39

    Article  Google Scholar 

  7. Gavrilovska L, Atanasovski VM (2013) Dynamic REM towards flexible spectrum management. In: International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services, Oct 2013, pp 287–296

    Google Scholar 

  8. Li S, Zhao Y, Sun C, Guo X (2014) Development of an advanced geolocation engine-based cognitive radio testbed. In: IEEE/CIC ICCC 2014 Symposium on Wireless Communications Systems, Oct 2014, pp 528–533

    Google Scholar 

  9. Yilmaz HB, Chae C-B, Tugcub T (2014) Sensor placement algorithm for radio environment map construction in cognitive radio networks. In: IEEE Wireless Communications and Networking Conference, Apr 2014, pp 2096–2101

    Google Scholar 

  10. Tajer A, Wang X (2009) Beacon-assisted spectrum access with cooperative cognitive transmitter and receiver. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2009, pp 2341–2344

    Google Scholar 

  11. Patel A, Biswas S, Jagannatham AK (2013) Multiple beacon based robust cooperative spectrum sensing in MIMO cognitive radio networks. In: IEEE Vehicular Technology Conference, Sept 2013, pp 1–5

    Google Scholar 

  12. Fitch M, Nekovee M, Kawade S, Briggs K, MacKenzie R (2011) Wireless services provision in TV white space with cognitive radio technology: a telecom operator’s perspective and experience. IEEE Commun Mag 49:64–73

    Article  Google Scholar 

  13. Ghasemi A, Sousa E (2007) Optimization of spectrum sensing for opportunistic spectrum access in cognitive radio networks. In: IEEE Consumer Communications and Networking Conference, Las Vegas, Jan 2007, pp 1022–1026

    Google Scholar 

  14. Unnikrishnan J, Veeravalli V (2007) Cooperative spectrum sensing and detection for cognitive radio. In: IEEE Global Telecommunications Conference, Washington, DC, Nov 2007, pp 2972–2976

    Google Scholar 

  15. Unnikrishnan J, Veeravalli VV (2008) Cooperative sensing for primary detection in cognitive radio. IEEE J Sel Top Signal Proces 2:18–27

    Article  Google Scholar 

  16. Ganesan G, Li Y (2005) Cooperative spectrum sensing in cognitive radio networks. In: IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Nov 2005, pp 137–143

    Article  Google Scholar 

  17. Akyildiz IF, Lo BF, Balakrishnan R (2011) Cooperative spectrum sensing in cognitive radio networks: a survey. Phys Commun (Elsevier) J 4:40–62

    Article  Google Scholar 

  18. Mishra S, Sahai A, Brodersen R (2006) Cooperative sensing among cognitive radios. In: IEEE International Conference on Communications, Istanbul, June 2006, vol 4, pp 1658–1663

    Google Scholar 

  19. Huang S, Liu X, Ding Z (2009) Optimal sensing-transmission structure for dynamic spectrum access. In: IEEE Conference on Computer Communications, Rio de Janeiro, Apr 2009, pp 2295–2303

    Google Scholar 

  20. Sahai A, Hoven N, Tandra R (2004) Some fundamental limits on cognitive radio. In: Allerton Conference on Communication, Control and Computing, Monticello, Oct 2004

    Google Scholar 

  21. Digham F, Alouini M, Simon M (2003) On the energy detection of unknown signals over fading channels. In: IEEE International Conference on Communications, Anchorage, May 2003, vol 5, pp 3575–3579

    Google Scholar 

  22. Koufos K (2013) Spectrum access in white spaces using spectrum sensing and geolocation databases. PhD thesis, Aalto University

    Google Scholar 

  23. Hossain E, Niyato D, Han Z (2009) Dynamic spectrum access and management in cognitive radio networks. Cambridge University Press, Amsterdam

    Book  Google Scholar 

  24. Varnamkhasti AG (2008) Spectrum sensing in cognitive wireless networks: requirements, challenges annd design trade-offs. PhD thesis, University of Toronto

    Google Scholar 

  25. Zeng Y, Liang Y, Hoang AT, Zhang R (2010) A review on spectrum sensing techniques for cognitive radio: challenges and solutions. EURASIP J Adv Signal Process 2010:1–15

    Article  Google Scholar 

  26. Haykin S, Thomson D, Reed J (2010) Spectrum sensing for cognitive radio. Proc IEEE 97:849–877

    Article  Google Scholar 

  27. Tandra R, Sahai A (2005) Some fundamental limits on detection in low SNR under noise uncertainty. In: IEEE International Conference on Wireless Networks, Communications and Mobile Computing, Wuhan, June 2005, vol 1, pp 464–469

    Google Scholar 

  28. Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutorials 11:116–130

    Article  Google Scholar 

  29. Cabric D, Mishra S, Brodersen R (2004) Implementation issues in spectrum sensing for cognitive radio. In: Asilomar Conference on Signals, Systems and Computers, Pacific Grove, Nov 2004, vol 1, pp 772–776

    Google Scholar 

  30. Arslan H (2007) Cognitive radio, software defined radio, and adaptive wireless systems. Springer, Dordrecht

    Book  Google Scholar 

  31. Urkowitz H (1967) Energy detection of unknown deterministic signal. Proc IEEE 55:523–531

    Article  Google Scholar 

  32. Ghasemi A, Sousa E (2005) Collaborative spectrum sensing for opportunistic access in fading environment. In: IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Nov 2005, pp 131–136

    Article  Google Scholar 

  33. Quan Z, Cui S, Sayed A (2008) Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE J Sel Top Signal Proces 2:28–40

    Article  Google Scholar 

  34. Quan Z, Cui S, Sayed A, Poor H (2008) Wideband spectrum sensing in cognitive radio networks. In: IEEE International Conference on Communications, Beijing, May 2008, pp 901–906

    Google Scholar 

  35. Tang H (2005) Some physical layer issues of wide-band cognitive radio systems. In: IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Nov 2005, pp 151–159

    Article  Google Scholar 

  36. Quan Z, Cui S, Sayed A, Poor H (2009) Optimal multiband joint detection for spectrum sensing in cognitive radio networks. IEEE Trans Signal Process 57:1128–1140

    Article  MathSciNet  Google Scholar 

  37. Cabric D, Brodersen R (2005) Physical layer design issues unique to cognitive radio systems. In: IEEE Personal Indoor and Mobile Radio Communications, Berlin, Sept 2005, vol 2, pp 759–763

    Google Scholar 

  38. Reyes H, Subramaniam S, Kaabouch N, Hu W (2016) A spectrum sensing technique based on autocorrelation and euclidean distance and its comparison with energy detection for cognitive radio networks. Elsevier Comput Electr Eng J 52:319–327

    Article  Google Scholar 

  39. Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 23:201–220

    Article  Google Scholar 

  40. Tian Z, Giannakis GB (2006) A wavelet approach to wideband spectrum sensing for cognitive radios. In: IEEE International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Mykonos Island, June 2006, pp 151–159

    Google Scholar 

  41. Tian Z, Giannakis G (2007) Compressed sensing for wideband cognitive radios. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Honolulu, Apr 2007, pp 1357–1360

    Google Scholar 

  42. Ganesan G, Li Y (2007) Cooperative spectrum sensing in cognitive radio: part I: two user networks. IEEE Trans Wirel Commun 6:2204–2213

    Article  Google Scholar 

  43. Shankar NS, Cordeiro C, Challapali K (2005) Spectrum agile radios: utilization and sensing architectures. In: IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Nov 2005, pp 160–169

    Article  Google Scholar 

  44. Ganesan G, Li Y (2007) Cooperative spectrum sensing in cognitive radio: part II: multiuser networks. IEEE Trans Wirel Commun 6:2214–2222

    Article  Google Scholar 

  45. Ahn KS, Jr Heath RW (2009) Performance analysis of maximum ratio combining with imperfect channel estimation in the presence of cochannel interferences. IEEE Trans Wirel Commun 8:1080–1085

    Article  Google Scholar 

  46. Song Y, Blostein SD, Cheng J (2003) Exact outage probability for equal gain combining with cochannel interference in Rayleigh Fading. IEEE Trans Wirel Commun 2:865–870

    Article  Google Scholar 

  47. Vistotsky E, Kuffner S, Peterson R (2005) On collaborative detection of TV transmissions in support of dynamic spectrum sharing. In: IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Nov 2005, pp 338–345

    Article  Google Scholar 

  48. Uchiyama H, Umebayashi K, Fujii T, Ono F, Sakaguchi K, Kamiya Y, and Suzuki Y (2008) Study on soft decision based cooperative sensing for cognitive radio networks. IEICE Trans Commun E91-B:95–101

    Article  Google Scholar 

  49. Ma J, Zhao G, Li Y (2008) Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. IEEE Trans Wirel Commun 7:4502–4507

    Article  Google Scholar 

  50. Oh DC, Lee HC, Lee YH (2010) Linear hard decision combining for cooperative spectrum sensing in cognitive radio systems. In: IEEE Vehicular Technology Conference, Ottawa, Sept 2010, pp 1–5

    Google Scholar 

  51. Shen B, Huang L, Zhao C, Kwak K, Zhou Z (2008) Weighted cooperative spectrum sensing in cognitive radio networks. In: International Conference on Convergence and Hybrid Information Technology, Busan, Nov 2008, vol 1, pp 1074–1079

    Google Scholar 

  52. Shahid MB, Kamruzzaman J (2008) Weighted soft decision for cooperative sensing in cognitive radio networks. In: IEEE International Conference on Networks, New Delhi, Dec 2008, pp 1–6

    Google Scholar 

  53. Zhao Y, Song M, Xin C (2011) A weighted cooperative spectrum sensing framework for infrastructure-based cognitive radio networks. Comput Commun 34:1510–1517

    Article  Google Scholar 

  54. Hasan N, Ejaz W, Kim HS (2012) PWAM: penalty-based weighted adjustment mechanism for cooperative spectrum sensing in centralized cognitive radios networks. Int J Innov Comput Inf Cont 8:1510–1517

    Google Scholar 

  55. Chuan L-qing, Zhi-ming W (2011) Adaptive weighted algorithm Of cooperative spectrum sensing in cognitive radio networks. In: IET International Communication Conference on Wireless Mobile and Computing, Shanghi, Nov 2011, pp 121–126

    Google Scholar 

  56. Zhou M, Chen H, Xie L, Wang K (2012) A reliable collaborative spectrum sensing scheme based on the ROCQ reputation model for cognitive radio networks, In: IEEE Vehicular Technology Conference, Quebec, May 2012, pp 1–5

    Google Scholar 

  57. Peh E, Liang Y (2007) Optimization for cooperative sensing in cognitive radio networks. In: IEEE Wireless Communications and Networking Conference, Hong Kong, Mar 2007, pp 27–32

    Google Scholar 

  58. Hossain E, Bhargava V (2007) Cognitive wireless communication networks. Springer, New York

    Book  Google Scholar 

  59. Liang Y, Zeng Y, Peh EC, Hoang AT (2008) Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans Wirel Commun 7:1326–1337

    Article  Google Scholar 

  60. Peh EC, Liang Y, Guan YL, Zeng Y (2010) Cooperative spectrum sensing in cognitive radio networks with weighted decision fusion schemes. IEEE Trans Wirel Commun 9:3838–3847

    Article  Google Scholar 

  61. Qihang P, Kun Z, Jun W, Shaoqian L (2006) A distributed spectrum sensing scheme based on credibility and evidence theory in cognitive radio context. In: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Helsinki, Sept 2006, pp 1–5

    Google Scholar 

  62. Nguyen-Thanh N, Koo I (2009) An enhanced cooperative spectrum sensing scheme based on evidence theory and reliability source evaluation in cognitive radio context. IEEE Commun Lett 13:492–494

    Article  Google Scholar 

  63. Feng J, Lu G, Bao Z (2012) Weighted-cooperative spectrum sensing scheme using trust in cognitive radio networks. In: IEEE International Conference on Signal Processing, Beijing, Oct 2012, pp 1693–1696

    Google Scholar 

  64. Qin T, Yu H, Leung C, Shen Z, Miao C (2009) Towards a trust aware cognitive radio architecture. ACM SIGMOBILE Mob Comput Commun Rev 13:86–95

    Article  Google Scholar 

  65. Khan Z, Lehtomaki J, Umebayashi K, Vartiainen J (2010) On the selection of the best detection performance sensors for cognitive radio networks. IEEE Signal Process Lett 17:359–362

    Article  Google Scholar 

  66. Zhang W, Mallik RK, Letaief KB (2008) Cooperative spectrum sensing optimization in cognitive radio networks. In: IEEE International Conference on Communications, Beijing, May 2008, pp 3411–3415

    Google Scholar 

  67. Li X, Li W, Hei Y (2012) Joint spectrum sensing and user selection strategy for cognitive radio networks. In: IEEE International Conference on Wireless Communications and Signal Processing, Huangshan, Oct 2012, pp 1–6

    Google Scholar 

  68. Jiang T, Qu D (2008) On minimum sensing error with spectrum sensing using counting rule in cognitive radio networks. In: International ICST Conference on Wireless Internet, Maui, Nov 2008, pp 1–9

    Google Scholar 

  69. Cacciapuoti AS, Akyildiz IF, Paura L (2012) Correlation-aware user selection for cooperative spectrum sensing in cognitive radio ad hoc networks. IEEE J Sel Areas Commun 30:297–306

    Article  Google Scholar 

  70. Do T, Mark BL (2009) Joint spatial-temporal spectrum sensing for cognitive radio networks. In: Conference on Information Sciences and Systems, Baltimore, Mar 2009, pp 124–129

    Google Scholar 

  71. Khalid L, Anpalagan A (2012) Cooperative sensing with correlated local decisions in cognitive radio networks. IEEE Trans Veh Technol 61:843–849

    Article  Google Scholar 

  72. Zeng K, Wang J, Li S, Cabric D (2011) Robust node selection for cooperative spectrum sensing with malicious users. In: IEEE Military Communications Conference, Baltimore, Nov 2011, pp 79–84

    Google Scholar 

  73. Wang W, Li H, Sun Y, Han Z (2010) Securing collaborative spectrum sensing against untrustworthy secondary users in cognitive radio networks. EURASIP J Adv Signal Process 2010:106–117

    Google Scholar 

  74. Song C, Zhang Q (2009) Achieving cooperative spectrum sensing in wireless cognitive radio networks. ACM SIGMOBILE Mob Comput Commun Spec Issue Cognit Radio Technol Syst 13:14–25

    Article  Google Scholar 

  75. Wang B, Liu K, Clancy T (2010) Evolutionary cooperative spectrum sensing game:how to collaborate? IEEE Trans Commun 58:890–900

    Article  Google Scholar 

  76. Li S, Zhu H, Yang B, Chen C, Guan X, Lin X (2012) Towards a game theoretical modeling of rational collaborative spectrum sensing in cognitive radio networks. In: IEEE Conference on Communications June 2012, pp 88–92

    Google Scholar 

  77. Lee WY, Akyildiz IF (2008) Optimal spectrum sensing framework for cognitive radio networks. IEEE Trans Wirel Commun 7:3845–3857

    Article  Google Scholar 

  78. Yu R, Zhang Y, Yi L, Xie S, Song L, and Guizani M (2012) Secondary users cooperation in cognitive radio networks: balancing sensing accuracy and efficiency. IEEE Wirel Commun 19:30–37

    Article  Google Scholar 

  79. Kim H, Shin KG (2008) Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. IEEE Trans Mob Comput 7:533–545

    Article  Google Scholar 

  80. Su H, Zhang X (2008) Cross-layer based opportunistic MAC protocols for QoS provisionings over cognitive radio wireless networks. IEEE J Sel Areas Commun 26:118–129

    Article  Google Scholar 

  81. Xie S, Liu Y, Zhang Y, Yu R (2010) A parallel cooperative spectrum sensing in cognitive radio networks. IEEE Trans Veh Technol 59:4079–4092

    Article  Google Scholar 

  82. Liu Y, Yu R, Zhang Y, Yuen C (2013) An efficient MAC protocol with selective grouping and cooperative sensing in cognitive radio networks. IEEE Trans Veh Technol 62:3928–3941

    Article  Google Scholar 

  83. Noel A, Schober R (2012) Convex sensing-reporting optimization for cooperative spectrum sensing. IEEE Trans Wire Commun 11:1900–1910

    Article  Google Scholar 

  84. Dai Z, Liu J, Long K (2015) Selective-reporting-based cooperative spectrum sensing strategies for cognitive radio networks. IEEE Trans Veh Technol 64:3043–3055

    Article  Google Scholar 

  85. Sun C, Zhang W, Letaief K (2007) Cooperative spectrum sensing for cognitive radios under bandwidth constraints. In: IEEE Wireless Communications and Networking Conference, Hong Kong, Mar 2007, pp 1–5

    Google Scholar 

  86. Pham H, Zhang Y, Engelstad P, Skeie T, Eliassen F (2010) Energy minimization approach for optimal cooperative spectrum sensing in sensor-aided cognitive radio networks. In: International ICST Conference on Wireless Internet, Singapore, Mar 2010, pp 1–9

    Google Scholar 

  87. Gao Y, Xu W, Yang K, Niu K, Lin J (2013) Energy-efficient transmission with cooperative spectrum sensing in cognitive radio networks. In: IEEE Wireless Communications and Networking Conference, Apr 2013, pp 7–12

    Google Scholar 

  88. Hu H, Zhang H, Liang Y-C (2016) On the spectrum- and energy-efficiency tradeoff in cognitive radio networks. IEEE Trans Commun 64(2):490–501

    Article  Google Scholar 

  89. Huang K (2013) Spatial throughput of mobile ad hoc networks powered by energy harvesting. IEEE Trans Inf Theory 59:7597–7612

    Article  Google Scholar 

  90. Lee S, Zhang R, Huang K (2013) Opportunistic wireless energy harvesting in cognitive radio networks. IEEE Trans Wirel Commun 12:4788–4799

    Article  Google Scholar 

  91. Park S, Kim H, Hong D (2013) Cognitive radio networks with energy harvesting. IEEE Trans Wirel Commun 12:1386–1397

    Article  Google Scholar 

  92. Chung W, Park S, Lim S, Hong D (2014) Spectrum sensing optimization for energy-harvesting cognitive radio systems. IEEE Trans Wirel Commun 13:2601–2613

    Article  Google Scholar 

  93. Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52:1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  94. Ragheb T, Kirolos S, Laska J, Gilbert A, Strauss M, Baraniuk R, Massoud Y (2007) Implementation models for analog-to-information conversion via random sampling. In: Midwest Symposium on Circuits and Systems, Montreal, Aug 2007, pp 325–328

    Google Scholar 

  95. Qin Z, Gao Y, Plumbley MD, Parini CG (2015) Wideband spectrum sensing on real time signals at sub-nyquist sampling rates in single and cooperative multiple nodes. IEEE Trans Signal Process 64(12):3106–3117

    Article  MathSciNet  Google Scholar 

  96. Paysarvi-Hoseini P, Beaulieu NC (2011) Optimal wideband spectrum sensing framework for cognitive radio systems. IEEE Trans Signal Process 59:1170–1182

    Article  MathSciNet  Google Scholar 

  97. Khalid L, Anpalagan A (2014) Adaptive grouping scheme for cooperative spectrum sensing in cognitive radio networks. In: IEEE Vehicular Technology Conference, May 2014, pp 1–5

    Google Scholar 

  98. Mishra S, Cabric D, Chang C, Willkomm D, van Schewick B, Wolisz A, Brodersen R (2005) A real time cognitive radio testbed for physical and link layer experiments. In: IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, Nov 2005, pp 8–11

    Google Scholar 

  99. Yuan Y, Bahl P, Chandra R, Chou P, Ferrell J, Moscibroda T, Narlanka S, Wu Y (2007) KNOWS: kognitiv networking over white spaces. In: IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, Apr 2007, pp 416–427

    Google Scholar 

  100. Gu S, Xu P, Wang X, Gan X, Yu H (2010) A real time testbed for the evaluation of cognitive radio MAC. In: IEEE Global Telecommunications Conference, Dec 2010, pp 1–5

    Google Scholar 

  101. Manna T, Misra IS (2013) Implementation of relay based collaborative spectrum sensing using coalitional games in wireless cognitive radio networks. Elsevier Comput Electr Eng J 45:77–99

    Article  Google Scholar 

  102. Rice University, “warp.rice.edu.” WARP homepage, 2013

    Google Scholar 

  103. Masri A, Chiasserini C-F, Casetti C, Perotti A (2012) Common control channel allocation in cognitive radio networks through UWB communication. J Commun Netw 14:710–718

    Article  Google Scholar 

  104. Chowdhury K, Akyildiz I (2011) OFDM based common control channel design for cognitive radio ad hoc networks. IEEE Trans Mob Comput 10:228–238

    Article  Google Scholar 

  105. Jia J, Zhang Q, Shen X (2008) HC-MAC: a hardware constrained cognitive MAC for efficient spectrum management. IEEE J Sel Areas Commun 26:106–117

    Article  Google Scholar 

  106. Zhou X, Ma J, Li GY, Kwon YH, Soong AC (2010) Probability-based combination for cooperative spectrum sensing. IEEE Trans Commun 58:463–466

    Article  Google Scholar 

  107. Chen Y, Beaulieu N (2009) Performance of collaborative spectrum sensing for cognitive radio in the presence of gaussian channel estimation errors. IEEE Trans Commun 57:1944–1947

    Article  Google Scholar 

  108. Zhang R, Wang M, Caiy LX, Zheng Z, Shen X, Xie L-L (2015) LTE-unlicensed: the future of spectrum aggregation for cellular networks. IEEE Wirel Commun 22:150–159

    Article  Google Scholar 

  109. Khan AA, Rehmani MH, Reisslein M (2015) Cognitive radio for smart grids: survey of architectures, spectrum sensing mechanisms, and networking protocols. IEEE Commun Surv Tutorials 18:860–898

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, Canada

    Lamiaa Khalid

  2. Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, Canada

    Alagan Anpalagan

Authors
  1. Lamiaa Khalid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alagan Anpalagan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lamiaa Khalid .

Editor information

Editors and Affiliations

  1. Electrical Engg Bldg, Rm 325, The University of New South Wales, Sydney, New South Wales, Australia

    Wei Zhang

Section Editor information

  1. School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore

    Dusit Niyato

  2. School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore

    Ping Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this entry

Cite this entry

Khalid, L., Anpalagan, A. (2017). Principles and Challenges of Cooperative Spectrum Sensing in Cognitive Radio Networks. In: Zhang, W. (eds) Handbook of Cognitive Radio . Springer, Singapore. https://doi.org/10.1007/978-981-10-1389-8_12-1

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-1389-8_12-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1389-8

  • Online ISBN: 978-981-10-1389-8

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation