Cognitive Transmit Beamforming From Binary CSIT

Transmit beamforming is used to steer radiated power towards a receiver of interest and to limit interference to unintended receivers, thereby facilitating coexistence. Transmit beamforming requires accurate channel state information (CSI) at the transmitter, which is often difficult to acquire, particularly in cognitive underlay settings, where the primary receiver cannot be expected to cooperate with the secondary system to enable it to learn the secondary to primary crosstalk channel. This paper considers cases where it is not realistic to assume channel reciprocity, or that the receivers are capable of accurate CSI estimation and feedback-because they are legacy systems, or have limited computation/energy resources. Transmit beamforming from binary and infrequent CSI is first considered for an isolated link. An online beamforming and learning algorithm is developed using the analytic center cutting plane method and is shown to asymptotically attain optimal performance. A robust maximum-likelihood formulation is next developed to handle feedback errors and correlation drift. The setup is then generalized to a cognitive underlay setting, also exploiting the standard acknowledgement/negative-acknowledgement feedback on the reverse primary link. This is the first solution to jointly tackle secondary signal-to-noise ratio maximization and primary interference mitigation from only rudimentary CSI, without assuming channel reciprocity.

[1]  Daniel Pérez Palomar,et al.  Rank-Constrained Separable Semidefinite Programming With Applications to Optimal Beamforming , 2010, IEEE Transactions on Signal Processing.

[2]  Andrea J. Goldsmith,et al.  Blind Null-Space Learning for MIMO Underlay Cognitive Radio with Primary User Interference Adaptation , 2013, IEEE Transactions on Wireless Communications.

[3]  Nikos D. Sidiropoulos,et al.  Convex Optimization-Based Beamforming , 2010, IEEE Signal Processing Magazine.

[4]  Halbert White,et al.  Estimation, inference, and specification analysis , 1996 .

[5]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[6]  Nikos D. Sidiropoulos,et al.  Cognitive transmit beamforming from binary link quality feedback for point to point MISO channels , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Shuzhong Zhang,et al.  New Results on Quadratic Minimization , 2003, SIAM J. Optim..

[8]  Jie Xu,et al.  Energy Beamforming With One-Bit Feedback , 2013, IEEE Transactions on Signal Processing.

[9]  Joseph Tabrikian,et al.  Marginal Likelihood for Estimation and Detection Theory , 2007, IEEE Transactions on Signal Processing.

[10]  Rui Zhang,et al.  On Active Learning and Supervised Transmission of Spectrum Sharing Based Cognitive Radios by Exploiting Hidden Primary Radio Feedback , 2009, IEEE Transactions on Communications.

[11]  Nikos D. Sidiropoulos,et al.  Sparse Conjoint Analysis Through Maximum Likelihood Estimation , 2013, IEEE Transactions on Signal Processing.

[12]  Georgios B. Giannakis,et al.  Design and analysis of transmit-beamforming based on limited-rate feedback , 2006, IEEE Transactions on Signal Processing.

[13]  James R. Zeidler,et al.  A simple gradient sign algorithm for transmit antenna weight adaptation with feedback , 2003, IEEE Trans. Signal Process..

[14]  Andrea J. Goldsmith,et al.  The One-Bit Null Space Learning Algorithm and Its Convergence , 2013, IEEE Transactions on Signal Processing.

[15]  G.B. Giannakis,et al.  Design and analysis of transmit-beamforming based on limited-rate feedback , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[16]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[17]  W. Newey,et al.  Large sample estimation and hypothesis testing , 1986 .

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

[19]  Aria Nosratinia,et al.  Cognitive Radio Protocols Based on Exploiting Hybrid ARQ Retransmissions , 2010, IEEE Transactions on Wireless Communications.

[20]  Ying-Chang Liang,et al.  Multi-antenna cognitive radio systems: Environmental learning and channel training , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[21]  Raghuraman Mudumbai,et al.  Distributed Transmit Beamforming Using Feedback Control , 2006, IEEE Transactions on Information Theory.

[22]  Robert W. Heath,et al.  Grassmannian beamforming for multiple-input multiple-output wireless systems , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[23]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[24]  Ying-Chang Liang,et al.  Cognitive beamforming made practical: Effective interference channel and learning-throughput tradeoff , 2008, 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications.

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