Sparse Array Beampattern Synthesis via Majorization-Based ADMM

TL;DR

This paper proposes a novel joint beampattern synthesis and sparse array construction method using majorization-based ADMM, effectively balancing array sparsity and pattern accuracy in large MIMO systems.

Contribution

It introduces a new optimization framework combining Shannon entropy with ADMM and MM techniques for sparse array synthesis, addressing nonconvex challenges.

Findings

Achieves a good trade-off between sparsity and pattern matching error.

Reduces runtime compared to benchmark methods.

Demonstrates effectiveness in large MIMO array scenarios.

Abstract

Beampattern synthesis is a key problem in many wireless applications. With the increasing scale of MIMO antenna array, it is highly desired to conduct beampattern synthesis on a sparse array to reduce the power and hardware cost. In this paper, we consider conducting beampattern synthesis and sparse array construction jointly. In the formulated problem, the beampattern synthesis is designed by minimizing the matching error to the beampattern template, and the Shannon entropy function is first introduced to impose the sparsity of the array. Then, for this nonconvex problem, an iterative method is proposed by leveraging on the alternating direction multiplier method (ADMM) and the majorization minimization (MM). Simulation results demonstrate that, compared with the benchmark, our approach achieves a good trade-off between array sparsity and beampattern matching error with less runtime.