Distributed Optimal Beamformers for Cognitive Radios Robust to Channel Uncertainties

摘要

Through spatial multiplexing and diversity, multi-input multi-output (MIMO)cognitive radio (CR) networks can markedly increase transmission rates andreliability, while controlling the interference inflicted to peer nodes andprimary users (PUs) via beamforming. The present paper optimizes the design oftransmit- and receive-beamformers for ad hoc CR networks when CR-to-CR channelsare known, but CR-to-PU channels cannot be estimated accurately. Capitalizingon a norm-bounded channel uncertainty model, the optimal beamforming design isformulated to minimize the overall mean-square error (MSE) from all datastreams, while enforcing protection of the PU system when the CR-to-PU channelsare uncertain. Even though the resultant optimization problem is non-convex,algorithms with provable convergence to stationary points are developed byresorting to block coordinate ascent iterations, along with suitable convexapproximation techniques. Enticingly, the novel schemes also lend themselvesnaturally to distributed implementations. Numerical tests are reported tocorroborate the analytical findings.