skip to main content
10.4108/icst.valuetools.2013.254385acmotherconferencesArticle/Chapter ViewAbstractPublication PagesvaluetoolsConference Proceedingsconference-collections
research-article

Adaptive spectrum management in MIMO-OFDM cognitive radio: an exponential learning approach

Published: 10 December 2013 Publication History

Abstract

In this paper, we examine cognitive radio systems that evolve dynamically over time as a function of changing user and environmental conditions. To take into account the advantages of orthogonal frequency division multiplexing (OFDM) and recent advances in multiple antenna (MIMO) technologies, we consider a full MIMO-OFDM Gaussian cognitive radio system where users with several antennas communicate over multiple non-interfering frequency bands. In this dynamic context, the objective of the network's secondary users (SUs) is to stay as close as possible to their optimum power allocation and signal covariance profile as it evolves over time, with only local channel state information at their disposal. To that end, we derive an adaptive spectrum management policy based on the method of matrix exponential learning, and we show that it leads to no regret (i.e. it performs asymptotically as well as any fixed signal distribution, no matter how the system evolves over time). As it turns out, this online learning policy is closely aligned to the direction of change of the users' data rate function, so the system's SUs are able to track their individual optimum signal profile even under rapidly changing conditions.

References

[1]
F. Alvarez, J. Bolte, and O. Brahic. Hessian Riemannian gradient flows in convex programming. SIAM Journal on Control and Optimization, 43(2):477--501, 2004.
[2]
A. Anandkumar, N. Michael, A. K. Tang, and A. Swami. Distributed algorithms for learning and cognitive medium access with logarithmic regret. IEEE J. Sel. Areas Commun., 29(4):731--745, April 2011.
[3]
H. Bölcskei, D. Gesbert, and A. J. Paulraj. On the capacity of OFDM-based spatial multiplexing systems. IEEE Trans. Commun., 50(2):225--234, February 2002.
[4]
G. Calcev, D. Chizhik, B. Göransson, S. Howard, H. Huang, A. Kogiantis, A. F. Molisch, A. L. Moustakas, D. Reed, and H. Xu. A wideband spatial channel model for system-wide simulations. IEEE Trans. Veh. Technol., 56(2):389, March 2007.
[5]
C. D. Cantrell. Modern mathematical methods for physicists and engineers. Cambridge University Press, Cambridge, UK, 2000.
[6]
N. Cesa-Bianchi and G. Lugosi. Prediction, Learning, and Games. Cambridge University Press, 2006.
[7]
P. Coucheney, B. Gaujal, and P. Mertikopoulos. Entropy-driven dynamics and robust learning procedures in games. http://arxiv.org/abs/1303.2270.
[8]
FCC Spectrum Policy Task Force. Report of the spectrum efficiency working group. Technical report, Federal Communications Comission, November 2002.
[9]
G. J. Foschini and M. J. Gans. On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Communications, 6:311--335, 1998.
[10]
Y. Gai, B. Krishnamachari, and R. Jain. Learning multiuser channel allocations in cognitive radio networks: A combinatorial multi-armed bandit formulation. In DySPAN '10: Proceedings of the 2010 IEEE Symposium on Dynamic Spectrum Access Networks, 2010.
[11]
A. Goldsmith, S. A. Jafar, I. Maric, and S. Srinivasa. Breaking spectrum gridlock with cognitive radios: An information theoretic perspective. Proc. IEEE, 97(5):894--914, 2009.
[12]
S. Haykin. Cognitive radio: Brain-empowered wireless communications. IEEE J. Sel. Areas Commun., 23(2):201--220, February 2005.
[13]
J. Hofbauer, S. Sorin, and Y. Viossat. Time average replicator and best reply dynamics. Mathematics of Operations Research, 34(2):263--269, May 2009.
[14]
J. Huang and Z. Han. Cognitive Radio Networks: Architectures, Protocols, and Standards, chapter Game theory for spectrum sharing. Auerbach Publications, CRC Press, 2010.
[15]
L. D. Landau and E. M. Lifshitz. Statistical physics. In Course of Theoretical Physics, volume 5. Pergamon Press, Oxford, 1976.
[16]
K. B. Letaief and Y. J. A. Zhang. Dynamic multiuser resource allocation and adaptation for wireless systems. Wireless Communications, IEEE, 13(4):38--47, August 2006.
[17]
H. Li. Multi-agent Q-learning of channel selection in multi-user cognitive radio systems: A two by two case. In SMC '09: Proceedings of the 2009 International Conference on Systems, Man and Cybernetics, pages 1893--1898, 2009.
[18]
P. Mertikopoulos, E. V. Belmega, and A. L. Moustakas. Matrix exponential learning: Distributed optimization in MIMO systems. In ISIT '12: Proceedings of the 2012 IEEE International Symposium on Information Theory, pages 3028--3032, 2012.
[19]
P. Mertikopoulos, E. V. Belmega, A. L. Moustakas, and S. Lasaulce. Dynamic power allocation games in parallel multiple access channels. In ValueTools '11: Proceedings of the 5th International Conference on Performance Evaluation Methodologies and Tools, 2011.
[20]
J. Mitola III and G. Q. Maquire Jr. Cognitive radio for flexible mobile multimedia communication. IEEE Personal Commun. Mag., 6(4):13--18, aug 1999.
[21]
N. Nie and C. Comaniciu. Adaptive channel allocation spectrum etiquette for cognitive radio networks. In DySPAN '05: Proceedings of the 2005 IEEE Symposium on Dynamic Spectrum Access Networks, pages 269--278, 2005.
[22]
D. P. Palomar, J. M. Cioffi, and M. Lagunas. Uniform power allocation in MIMO channels: a game-theoretic approach. IEEE Trans. Inf. Theory, 49(7):1707, July 2003.
[23]
R. T. Rockafellar. Convex Analysis. Princeton University Press, Princeton, NJ, 1970.
[24]
W. H. Sandholm. Population Games and Evolutionary Dynamics. Economic learning and social evolution. MIT Press, Cambridge, MA, 2010.
[25]
K. V. Schinasi. Spectrum management: Better knowledge needed to take advantage of technologies that may improve spectrum efficiency. Technical report, United States General Accounting Office, May 2004.
[26]
G. Scutari and D. P. Palomar. MIMO cognitive radio: A game theoretical approach. IEEE Trans. Signal Process., 58(2):761--780, February 2010.
[27]
S. Shalev-Shwartz. Online learning and online convex optimization. Foundations and Trends in Machine Learning, 4(2):107--194, 2011.
[28]
S. Sorin. Exponential weight algorithm in continuous time. Mathematical Programming, 116(1):513--528, 2009.
[29]
C. R. Stevenson, G. Chouinard, Z. Lei, W. Hu, and S. J. Shellhammer. IEEE 802.22: The first cognitive radio wireless regional area network standard. IEEE Commun. Mag., 47(1):130--138, jan 2009.
[30]
P. D. Taylor and L. B. Jonker. Evolutionary stable strategies and game dynamics. Mathematical Biosciences, 40(1-2):145--156, 1978.
[31]
I. E. Telatar. Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications and Related Technologies, 10(6):585--596, 1999.
[32]
J. Wang, G. Scutari, and D. P. Palomar. Robust MIMO cognitive radio via game theory. IEEE Trans. Signal Process., 59(3):1183--1201, March 2011.
[33]
Y. J. A. Zhang and M.-C. A. So. Optimal spectrum sharing in MIMO cognitive radio networks via semidefinite programming. IEEE J. Sel. Areas Commun., 29(2):362--373, 2011.
[34]
Q. Zhao and B. M. Sadler. A survey of dynamic spectrum access. IEEE Signal Process. Mag., 24(3):79--89, May 2007.

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ValueTools '13: Proceedings of the 7th International Conference on Performance Evaluation Methodologies and Tools
December 2013
336 pages
ISBN:9781936968480

Sponsors

  • EAI: The European Alliance for Innovation
  • ICST

In-Cooperation

Publisher

ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

Brussels, Belgium

Publication History

Published: 10 December 2013

Check for updates

Qualifiers

  • Research-article

Conference

ValueTools '13
Sponsor:
  • EAI

Acceptance Rates

Overall Acceptance Rate 90 of 196 submissions, 46%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 13 Jan 2025

Other Metrics

Citations

Cited By

View all

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media