Multi-Target Integrated Sensing and Communications in Massive MIMO Systems

TL;DR

This paper investigates multi-target sensing in massive MIMO systems, proposing precoding schemes that balance sensing accuracy and communication needs, with a focus on minimizing estimation bounds and optimizing resource sharing.

Contribution

It introduces novel precoding strategies for multi-target ISAC in massive MIMO, demonstrating near-optimal sensing performance with reduced complexity and effective resource sharing techniques.

Findings

Sensing-focused precoding with power allocation achieves near-optimal sensing performance.

Time-sharing between sensing and communication outperforms simple time division.

Proposed schemes significantly reduce complexity compared to joint precoding.

Abstract

Integrated sensing and communications (ISAC) allows networks to perform sensing alongside data transmission. While most ISAC studies focus on single-target, multi-user scenarios, multi-target sensing is scarcely researched. This letter examines the monostatic sensing performance of a multi-target massive MIMO system, aiming to minimize the sum of Cram\'er-Rao lower bounds (CRLBs) for target direction-of-arrival estimates while meeting user equipment (UE) rate requirements. We propose several precoding schemes, comparing sensing performance and complexity, and find that sensing-focused precoding with power allocation for communication achieves near-optimal performance with 20 times less complexity than joint precoding. Additionally, time-sharing between communication and sensing outperforms simple time division, highlighting the benefits of resource-sharing for ISAC.