Phase-Coherent D-MIMO ISAC: Multi-Target Estimation and Spectral Efficiency Trade-Offs

TL;DR

This paper explores a distributed MIMO system for integrated sensing and communication, proposing a two-stage estimation framework and adaptive AP strategies to optimize spectral efficiency and sensing accuracy.

Contribution

It introduces a novel two-stage ML estimation framework and adaptive AP mode-selection strategies for balancing sensing and communication in D-MIMO ISAC systems.

Findings

High-accuracy multi-target estimation achieved with the proposed framework.

Trade-offs between spectral efficiency and sensing precision demonstrated.

Optimal number of APs identified for maximizing sensing coverage.

Abstract

We investigate distributed multiple-input multiple-output (D-MIMO) integrated sensing and communication (ISAC) systems, in which multiple phase-synchronized access points (APs) jointly serve user equipments (UEs) while cooperatively detecting and estimating multiple static targets. To achieve high-accuracy multi-target estimation, we propose a two-stage sensing framework combining non-coherent and coherent maximum-likelihood (ML) estimation. In parallel, adaptive AP mode-selection strategies are introduced to balance communication and sensing performance: a communication-centric scheme that maximizes downlink spectral efficiency (SE) and a sensing-centric scheme that selects geometrically diverse receive APs to enhance sensing coverage. Simulation results confirm the SE-sensing trade-off, where appropriate power allocation between communication and sensing and larger array apertures alleviate performance degradation, achieving high SE with millimeter-level sensing precision. We further demonstrate that the proposed AP-selection strategy reveals an optimal number of receive APs that maximizes sensing coverage without significantly sacrificing SE.