A rollout-based joint spectrum sensing and access policy for cognitive radio networks with hardware limitations

The practical hardware limitations bring technical challenges to cognitive radio, e.g. limited capability of spectrum sensing and certain frequency range of spectrum access. In this paper, we propose a rollout-based joint spectrum sensing and access policy incorporating the hardware limitations of both sensing capability and spectrum aggregation, in which the optimal policy is shown to be PSPACE-hard. Two heuristic policies are proposed to serve as base policies, based on which the developed rollout-based policy approximates the value function and determines the appropriate spectrum sensing and access actions. We establish mathematically that the rollout-based policy achieves better performance than the base policy. We also demonstrate that the low-complexity rollout-based policy leads to only slight performance loss compared with the optimal policy.

[1]  Dimitri P. Bertsekas,et al.  Rollout Algorithms for Stochastic Scheduling Problems , 1999, J. Heuristics.

[2]  B. Gnedenko,et al.  Limit Distributions for Sums of Independent Random Variables , 1955 .

[3]  Danijela Cabric,et al.  Performance of Joint Spectrum Sensing and MAC Algorithms for Multichannel Opportunistic Spectrum Access Ad Hoc Networks , 2009, IEEE Transactions on Mobile Computing.

[4]  Gerald Tesauro,et al.  On-line Policy Improvement using Monte-Carlo Search , 1996, NIPS.

[5]  G. Monahan State of the Art—A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms , 1982 .

[6]  D.A. Castanon,et al.  Rollout Algorithms for Stochastic Scheduling Problems , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[7]  Wei Wang,et al.  A POMDP-based optimal spectrum sensing and access scheme for cognitive radio networks with hardware limitation , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[8]  Wei Wang,et al.  Spectrum Aggregation: Overview and Challenges , 2010, Netw. Protoc. Algorithms.

[9]  D. Braziunas POMDP solution methods , 2003 .

[10]  George E. Monahan,et al.  A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms , 2007 .

[11]  John N. Tsitsiklis,et al.  Neuro-dynamic programming: an overview , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[12]  Edward J. Sondik,et al.  The Optimal Control of Partially Observable Markov Processes over a Finite Horizon , 1973, Oper. Res..

[13]  Guanding Yu,et al.  Prediction-Based Spectrum Aggregation with Hardware Limitation in Cognitive Radio Networks , 2010, 2010 IEEE 71st Vehicular Technology Conference.

[14]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[15]  Ananthram Swami,et al.  Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework , 2007, IEEE Journal on Selected Areas in Communications.

[16]  Kang G. Shin,et al.  Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks , 2008, IEEE Transactions on Mobile Computing.

[17]  John N. Tsitsiklis,et al.  Rollout Algorithms for Combinatorial Optimization , 1997, J. Heuristics.