Toward ISAC-empowered subnetworks: Cooperative localization and iterative node selection

TL;DR

This paper introduces a low-complexity iterative node selection algorithm for ISAC subnetworks, significantly improving localization accuracy and robustness while analyzing the sensing-communication trade-off.

Contribution

It proposes a novel iterative node selection method that enhances localization accuracy and robustness in ISAC subnetworks under resource constraints.

Findings

Achieves sub-7 cm localization accuracy within three iterations.

Over 97% improvement over benchmarks under the same sensing budget.

Increasing spatial diversity improves sensing robustness in fading channels.

Abstract

This paper tackles the sensing-communication trade-off in integrated sensing and communication (ISAC)-empowered subnetworks for mono-static target localization. We propose a low-complexity iterative node selection algorithm that exploits the spatial diversity of subnetwork deployments and dynamically refines the set of sensing subnetworks to maximize localization accuracy under tight resource constraints. Simulation results show that our method achieves sub-7 cm accuracy in additive white Gaussian noise (AWGN) channels within only three iterations, yielding over 97% improvement compared to the best-performing benchmark under the same sensing budget. We further demonstrate that increasing spatial diversity through additional antennas and subnetworks enhances sensing robustness, especially in fading channels. Finally, we quantify the sensing-communication trade-off, showing that reducing sensing iterations and the number of sensing subnetworks improves throughput at the cost of reduced localization precision.