MIMO Beamforming Design towards Maximizing Mutual Information in Wireless Sensor Network

TL;DR

This paper introduces two novel beamforming algorithms for wireless sensor networks that maximize mutual information, overcoming limitations of previous methods through convex optimization and analytical solutions, with proven convergence and improved performance.

Contribution

The paper proposes two new beamforming design methods using block coordinate ascent that are more general, efficient, and have guaranteed convergence, addressing limitations of existing approaches.

Findings

The proposed methods outperform existing algorithms in simulations.

The new algorithms have guaranteed convergence properties.

Analytical solutions reduce computational complexity.

Abstract

This paper considers joint beamformer design towards maximizing the mutual information in a coherent wireless sensor network with noisy observation and multiple antennae. Leveraging the weighted minimum mean square error and block coordinate ascent (BCA) framework, we propose two new and efficient methods: batch-mode BCA and cyclic multi-block BCA. The existing batch-mode approaches require stringent conditions such as diagonal channel matrices and positive definite second-order matrices, and are therefore inapplicable to our problem. Our match-mode BCA overcomes the previous limitations via a general second-order cone programming formation, and exhibits a strong convergence property which we have rigorously proven. The existing multi-block approaches rely on numerical solvers to handle the subproblems and some render good performance only at high signal-to-noise ratios. Exploiting the convexity of the trust-region subproblem for the convex case, our multi-block BCA significantly reduces the complexity and enhances the previous results by providing an analytical expression for the energy-preserving optimal solution. Analysis and simulations confirm the advantages of the proposed methods.