Opportunistic spectrum access for energy-constrained cognitive radios

This paper considers a scenario in which a secondary user (SU) opportunistically accesses a channel allocated to some primary network (PN) that switches between idle and active states in a time-slotted manner. At the beginning of each time slot, SU can choose to stay idle or to carry out spectrum sensing to detect the state of PN. If PN is detected to be idle, SU can carry out data transmission. Spectrum sensing consumes time and energy and introduces false alarms and mis-detections. The objective is to dynamically decide, for each time slot, whether SU should stay idle or carry out sensing, and if so, for how long, to maximize the expected reward. We formulate this as a partially observable Markov decision process and prove important properties of the optimal control policies. Heuristic control policies with low complexity and good performance are also proposed. Numerical results show the significant performance gain of our dynamic control approach for opportunistic spectrum access.

[1]  Qing Zhao,et al.  Distributed Cognitive MAC for Energy-Constrained Opportunistic Spectrum Access , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[2]  Ying-Chang Liang,et al.  Adaptive Scheduling of Spectrum Sensing Periods in Cognitive Radio Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[3]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Trans. Wirel. Commun..

[4]  Israel Bar-David,et al.  Capacity and coding for the Gilbert-Elliot channels , 1989, IEEE Trans. Inf. Theory.

[5]  S. Christian Albright,et al.  Structural Results for Partially Observable Markov Decision Processes , 1979, Oper. Res..

[6]  Amir Ghasemi,et al.  Optimization of Spectrum Sensing for Opportunistic Spectrum Access in Cognitive Radio Networks , 2007, 2007 4th IEEE Consumer Communications and Networking Conference.

[7]  Benjamin Paul Jordan On optimal search for a moving target , 1995 .

[8]  William S. Lovejoy,et al.  Some Monotonicity Results for Partially Observed Markov Decision Processes , 1987, Oper. Res..

[9]  Yiyang Pei,et al.  Sensing-Throughput Tradeoff in Cognitive Radio Networks: How Frequently Should Spectrum Sensing be Carried Out? , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[10]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[11]  Ananthram Swami,et al.  A Decision-Theoretic Framework for Opportunistic Spectrum Access , 2007, IEEE Wireless Communications.

[12]  William S. Lovejoy,et al.  Computationally Feasible Bounds for Partially Observed Markov Decision Processes , 1991, Oper. Res..

[13]  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.

[14]  Vikram Krishnamurthy,et al.  Opportunistic file transfer over a fading channel: A POMDP search theory formulation with optimal threshold policies , 2006, IEEE Transactions on Wireless Communications.

[15]  Danlu Zhang,et al.  Transmission schemes for time-varying wireless channels with partial state observations , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[16]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.