Joint Sensing-Channel Selection and Power Control for Cognitive Radios

We consider joint optimization for sensing-channel selection and ensuing power control problem with cognitive radios over time-varying fading channels. It is shown that this joint design can be judiciously formulated as a convex optimization problem. Optimal joint sensing-channel selection and power control scheme is then derived in closed-form under the constraints of average power budget and maximum allowable probability of collisions with the primary communications. In addition, we develop a stochastic optimization algorithm that can operate without a-priori knowledge of the fading channel statistics. It is rigourously established that the proposed stochastic scheme is capable of dynamically learning the intended wireless channels on-the-fly to approach the optimal strategy almost surely. Numerous results are also provided to evaluate the proposed schemes for cognitive transmissions over block fading channels.

[1]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[2]  Andrea J. Goldsmith,et al.  Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective , 2009, Proceedings of the IEEE.

[3]  Khaled Ben Letaief,et al.  Multiuser OFDM with adaptive subcarrier, bit, and power allocation , 1999, IEEE J. Sel. Areas Commun..

[4]  Yuguang Fang,et al.  Stochastic Channel Selection in Cognitive Radio Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[5]  Xin Wang,et al.  Dynamic Resource Management for Cognitive Radios Using Limited-Rate Feedback , 2009, IEEE Transactions on Signal Processing.

[6]  ModianoEytan,et al.  Fairness and optimal stochastic control for heterogeneous networks , 2008 .

[7]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[8]  Ananthram Swami,et al.  Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Errors , 2007, IEEE Transactions on Information Theory.

[9]  Sriram Vishwanath,et al.  On the Fundamental Limits of Interweaved Cognitive Radios , 2009, ArXiv.

[10]  Anant Sahai,et al.  SNR Walls for Signal Detection , 2008, IEEE Journal of Selected Topics in Signal Processing.

[11]  G.B. Giannakis,et al.  Optimum scheduling for orthogonal multiple access over fading channels using quantized channel state information , 2008, 2008 IEEE 9th Workshop on Signal Processing Advances in Wireless Communications.

[12]  Alejandro Ribeiro,et al.  Ergodic Stochastic Optimization Algorithms for Wireless Communication and Networking , 2010, IEEE Transactions on Signal Processing.

[13]  Xin Wang,et al.  A Unified Approach to QoS-Guaranteed Scheduling for Channel-Adaptive Wireless Networks , 2007, Proceedings of the IEEE.

[14]  R. Srikant,et al.  Fair Resource Allocation in Wireless Networks Using Queue-Length-Based Scheduling and Congestion Control , 2005, IEEE/ACM Transactions on Networking.

[15]  Michael J. Neely,et al.  Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

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

[17]  Xin Wang,et al.  Power-Efficient Resource Allocation for Time-Division Multiple Access Over Fading Channels , 2008, IEEE Transactions on Information Theory.

[18]  Alexander L. Stolyar,et al.  Maximizing Queueing Network Utility Subject to Stability: Greedy Primal-Dual Algorithm , 2005, Queueing Syst. Theory Appl..