Efficient RF Chain Selection for MIMO Integrated Sensing and Communications: A Greedy Approach

TL;DR

This paper introduces a low-complexity greedy RF chain selection framework for MIMO ISAC systems that maximizes a unified mutual information metric, improving efficiency while maintaining near-optimal performance.

Contribution

It proposes two novel greedy RF chain selection methods, GES and GCS, and extends the framework to beam selection with DBS, reducing complexity in MIMO ISAC systems.

Findings

Achieves near-optimal performance with lower complexity than exhaustive search.

Effective RF chain and beam selection methods for MIMO ISAC systems.

Demonstrates practical applicability through simulation results.

Abstract

In multiple-input multiple-output integrated sensing and communication (MIMO ISAC) systems, radio frequency chain (i.e., RF chain) selection plays a vital role in reducing hardware cost, power consumption, and computational complexity. However, designing an effective RF chain selection strategy is challenging due to the disparity in performance metrics between communication and sensing-mutual information (MI) versus beam-pattern mean-squared error (MSE) or the Cram\'er-Rao lower bound (CRLB). To overcome this, we propose a low-complexity greedy RF chain selection framework maximizing a unified MI-based performance metric applicable to both functions. By decomposing the total MI into individual contributions of each RF chain, we introduce two approaches: greedy eigen-based selection (GES) and greedy cofactor-based selection (GCS), which iteratively identify and remove the RF chains with the lowest contribution. We further extend our framework to beam selection for beamspace MIMO ISAC systems, introducing diagonal beam selection (DBS) as a simplified solution. Simulation results show that our proposed methods achieve near-optimal performance with significantly lower complexity than exhaustive search, demonstrating their practical effectiveness for MIMO ISAC systems.