Antenna Selection in Switch-Based MIMO Arrays via DOA threshold region Approximation

TL;DR

This paper introduces novel antenna selection algorithms for switch-based MIMO arrays that optimize DOA estimation performance by leveraging a DOA threshold region approximation, improving accuracy and reducing complexity.

Contribution

It proposes a new subarray layout alignment and two antenna selection algorithms, including a deep-learning-based method, for enhanced DOA estimation in switch-based MIMO systems.

Findings

Significant performance improvement over benchmarks in DOA estimation accuracy.

Reduced computational complexity of antenna selection algorithms.

Effective use of DOA threshold region approximation for array optimization.

Abstract

Direction-of-arrival (DOA) information is vital for multiple-input-multiple-output (MIMO) systems to complete localization and beamforming tasks. Switched antenna arrays have recently emerged as an effective solution to reduce the cost and power consumption of MIMO systems. Switch-based array architectures connect a limited number of radio frequency chains to a subset of the antenna elements forming a subarray. This paper addresses the problem of antenna selection to optimize DOA estimation performance. We first perform a subarray layout alignment process to remove subarrays with identical beampatterns and create a unique subarray set. By using this set, and based on a DOA threshold region performance approximation, we propose two antenna selection algorithms; a greedy algorithm and a deep-learning-based algorithm. The performance of the proposed algorithms is evaluated numerically. The results show a significant performance improvement over selected benchmark approaches in terms of DOA estimation in the threshold region and computational complexity.