Distributed Optimal Beamformers for Cognitive Radios Robust to Channel Uncertainties

TL;DR

This paper develops robust distributed beamforming algorithms for MIMO cognitive radio networks that optimize performance under channel uncertainties, ensuring primary user protection while improving transmission quality.

Contribution

It introduces a novel robust beamforming design for CR networks with channel uncertainty, employing convex approximation and distributed algorithms with convergence guarantees.

Findings

Algorithms converge to stationary points.

Distributed implementation is feasible.

Numerical results confirm robustness and efficiency.

Abstract

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