Journal of Communications, Vol 7, No 7 (2012), 514-523, Jul 2012
doi:10.4304/jcm.7.7.514-523

Cognitive Radio: Forging ahead from Concept, Testbed to Large-Scale Deployment

Youping Zhao, Lizdabel Morales-Tirado

Abstract


This article first briefly reviews the basic concepts about cognitive radio (CR) technology and CR-enabled cognitive wireless communications, and then discusses the differentiating features of CR and the fundamental theories or principles behind CR, especially from the artificial intelligence and machine learning perspective. As a CR testbed plays an important role in the transition of CR technology from concept to large-scale deployment, a survey on the state-of-the-art development on CR testbeds is presented, which indicates the trend of future development. Emerging research topics and potential applications of CR technology in various areas are also discussed which could be further investigated in an innovative CR approach.


Keywords


artificial intelligence; cognitive engine; cognitive radio; cognitive wireless communications; machine learning; testbeds

References


 

[1] J. Mitola III and G. Q. Maguire Jr., “Cognitive Radio: Making Software Radios More Personal,” IEEE Personal Communications, vol. 6, no. 4, pp.13–18, Aug. 1999.
http://dx.doi.org/10.1109/98.788210

[2] J. Mitola III, “Cognitive Radio-An Integrated Agent Architecture for Software Defined Radio,” Ph.D. dissertation, Royal Institute of Technology (KTH), Stockholm, Sweden, 2000.

[3] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, Feb. 2005, pp. 201–220.
http://dx.doi.org/10.1109/JSAC.2004.839380

[4] F. K. Jondral, “Software-Defined Radio–Basics and Evolution to Cognitive Radio,” EURASIP Journal on Wireless Communications and Networking, pp. 275–283, March 2005.

[5] J. H. Reed and C. W. Bostian, “Understanding the Issues in Software Defined Cognitive Radio,” Tutorial for IEEE DySPAN 2005, Baltimore, MD, Nov. 2005.

[6] DARPA XG WG, “The XG Architectural Framework V1.0,” tech. rep., DARPA, 2003.

[7] BBN Technologies, “The XG Vision Request for Comments (Version 2.0),” [online] Available: http://www.ir.bbn.com/projects/xmac/index.html (2004).

[8] Y. Zhao, B. Le, J. H. Reed, “Network Support – The Radio Environment Map,” Cognitive Radio Technology, Chapter 11, pp. 325–366, Elsevier, 2009 (2nd Ed.)

[9] A. He, K. K. Bae, J. Gaeddert, R. Menon, L. Morales, J. Neel, Y. Zhao, J. H. Reed, W. Tranter, “A Survey of Artificial Intelligence for Cognitive Radios,” IEEE Trans.Vehicular Technology, vol. 59, no. 4, May 2010, pp. 1578–1592.
http://dx.doi.org/10.1109/TVT.2010.2043968

[10] N. P. Padhy, Artificial Intelligence and Intelligent Systems, Oxford University Press, 2005

[11] S. M. Kay, Statistical Signal Processing–Estimation Theory, Prentice Hall, 1993

[12] K. J. R. Liu, “Cognitive Radio Games,” IEEE Spectrum, vol. 48, no. 4, pp.41–43, April 2011
http://dx.doi.org/10.1109/MSPEC.2011.5738398

[13] B. Wang, Y. Wu, and K.J. Ray Liu. “Game theory for cognitive radio networks: An overview,” Comput. Netw. 54, 14 (October 2010), 2537-2561.
http://dx.doi.org/10.1016/j.comnet.2010.04.004

[14] T. W. Rondeau, B. Le, D. Maldonado, D. Scaperoth, and C. W. Bostian, “Cognitive radio formulation and implementation,” in Proc. CROWNCOM 2006, pp. 1–8, June 2006.

[15] R. W. Brodersen, A. Wolisz, D. Cabric, S. M. Mishra, and D. Willkomm, “CORVUS: A cognitive radio approach for usage of virtual unlicensed spectrum,” tech. rep., The Berkeley Wireless Research Center, July 2004.

[16] S. Mishra, D. Cabric, C. Chang, D. Willkomm, B. van Schewick, S. Wolisz, and B. Brodersen, “A real time cognitive radio testbed for physical and link layer experiments,” in Proc. DySPAN 2005, pp. 562–567, November 2005.

[17] OSSIE, "http://ossie.wireless.vt.edu." Published Online.

[18] J. H. Reed, C. Dietrich, J. Gaeddert, K. Kim, R. Menon, L. Morales, and Y. Zhao, “Development of a cognitive engine and analysis of WRAN cognitive radio algorithms,” tech. rep., Virginia Tech., December 2005.

[19] E. Stuntebeck, T. O'Shea, J. Hecker, and T. C. Clancy, “Architecture for an open source cognitive radio,” in Proc. SDR Forum Technical Conference, November 2006.

[20] SOAR, http://sitemaker.umich.edu/soar/home. Published Online.

[21] C. Clancy, J. Hecker, E. Stuntebeck, and T. O'Shea, “Applications of machine learning to cognitive radio networks,” IEEE Trans. Wireless Communications, vol. 14, pp. 47-52, August 2007.
http://dx.doi.org/10.1109/MWC.2007.4300983

[22] Z. Miljanic, I. Seskar, K. Le, and D. Raychaudhuri, “The WINLAB Network Centric Cognitive Radio Hardware Platform: WiNC2R," in Proc. CROWNCOM 2007, pp. 155-160, August 2007.

[23] T. R. Newman, B. Barker, A. Wyglinski, A. Agah, G. J. Minden, and J. B. Evans, “Cognitive engine implementation for wireless multi-carrier transceivers,” pp. 1–14. John Wiley & Sons, Ltd., May 2007.

[24] N. Baldo and M. Zorzi, “Fuzzy logic for cross-layer optimization in cognitive radio networks,” in 4th IEEE Consumer Communications and Networking Conference, pp. 1128-1133, January 2007.
http://dx.doi.org/10.1109/CCNC.2007.227

[25] N. Baldo and M. Zorzi, “Learning and adaptation in cognitive radios using neural networks,” in IEEE CCNC, pp. 998-1003, Jan. 2008.

[26] Ettus Research, “Universal Software Radio Peripheral”, [online] Available: http://www.ettus.com

[27] Rice University, “Wireless Open Access Research Platform”, [online] Available: http://warp.rice.edu/

[28] A. Sanchez, et al, “Testbed Federation: An Approach for Experimentation-driven Research in Cognitive Radios and Cognitive Networking,” Future Network and Mobile Summit, Warsaw, Poland, 15-17 June 2011.

[29] VT-CORNET, [online], http://cornet.wireless.vt.edu/trac/wiki/CORNET

[30] ORBIT, [online], http://www.orbit-lab.org/wiki/WikiStart

[31] Emulab, [online], http://www.emulab.net/

[32] CREW, [online], http://www.crew-project.eu/

[33] Y. Zhao, D. Raymond, C. da Silva, J. H. Reed, and S. Midkiff, “Performance Evaluation of Radio Environment Map Enabled Cognitive Spectrum-Sharing Networks,” in Proc. MILCOM 2007, Oct. 29-31, 2007, Orlando, FL.
http://dx.doi.org/10.1109/MILCOM.2007.4454765

[34] Y. Zhao, J. Gaeddert, L. Morales, K. K. Bae, and J. H. Reed, “Development of Radio Environment Map Enabled Case- and Knowledge-Based Learning Algorithms for IEEE 802.22 WRAN Cognitive Engines,” in Proc. CROWNCOM 2007, pp.44–49, Aug.1-3, 2007, Orlando, FL.

[35] Y. Zhao, L. Morales, J. Gaeddert, K. K. Bae, J. Um, and J. H. Reed, “Applying Radio Environment Map to Cognitive Wireless Regional Area Networks,” in Proc. DySPAN 2007, pp.115–118, April 17-20, 2007, Dublin, Ireland.

[36] Y. Zhao, J. Gaeddert, K. K. Bae, and J. H. Reed, “Radio Environment Map-Enabled Situation-Aware Cognitive Radio Learning Algorithms,” in Proc. SDR Forum Technical Conference, Nov., 2006, Orlando, FL.

[37] Y. Zhao, S. Mao, J. Neel, and J. H. Reed, “Performance Evaluation of Cognitive Radios: Metrics, Utility Functions and Methodologies,” Proceedings of the IEEE, Special Issue on Cognitive Radio, pp.642–659, vol.97, no. 4, April 2009.

[38] Y. Zhao, S. Mao, J. H. Reed, Y. Huang, “Utility Function Selection for Streaming Videos with a Cognitive Engine Testbed,” ACM/Springer MONET, Special Issue on Advances in Wireless Testbeds and Research Infrastructures, pp. 446–460, vol. 15, no. 3, 2010.

[39] K. Tan, et al., “Sora: High Performance Software Radio using General Purpose Multi-core Processors,” USENIX NSDI 2009, Apr. 2009, Boston, MA.

[40] Morales-Tirado, L.; Suris-Pietri, J.E.; Reed, J.H., "A Hybrid Cognitive Engine for Improving Coverage in 3G Wireless Networks," IEEE International Conference on Communications Workshops, vol., no., pp.1-5, 14-18 June 2009

[41] Bruce Fette (editor), Cognitive Radio Technology, second edition, Elsevier, 2009.

[42] WARP: http://warp.rice.edu/

[43] CORAL: http://www.crc-coral.com/

[44] J. Wang, M. Ghosh, K. Challapali, “Emerging cognitive radio applications: A survey,” IEEE Communications Magazine, March 2011, vol: 49, no:3, pp. 74–81.

[45] L. Morales, “An Approach to Using Cognition in Wireless Networks, Dissertation”, Virginia Polytechnic Institute and State University, December 2009.

[46] T. Dietterich and P. Langley, Cognitive Networks, ch. Machine Learning for Cognitive Networks: Technology Assessment and Research Challenges. John Wiley & Sons, Ltd., 2007.

[47] P. Langley and H. A. Simon, “Applications of machine learning and rule induction,” Communications of the ACM, vol. 38, pp. 54-64, November 1995.
http://dx.doi.org/10.1145/219717.219768


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