Radio Stripe-Based Distributed ISAC System with Dynamic Sensing-Communication Reconfiguration

TL;DR

This paper explores a radio stripe-based distributed ISAC system that dynamically reconfigures nodes for sensing or communication, analyzing the trade-offs between localization accuracy and data rate in a 6G context.

Contribution

It proposes a low-complexity distributed ISAC architecture with dynamic mode switching and a batch estimation method for target localization.

Findings

Increasing devices improves sensing accuracy but reduces sum rate.

Sum rate remains constant regardless of device positions for fixed communication APUs.

Changing antenna numbers has a non-monotonic effect on sensing performance.

Abstract

Integrated sensing and communications (ISAC) has emerged as an intrinsic service of upcoming 6G wireless systems, enabling the reuse of communication signals for environmental sensing and supporting context-aware network functionalities. Meanwhile, the evolution of the wireless infrastructure toward distributed systems creates new opportunities for collaborative sensing from spatially separated nodes. Motivated by this trend, this work investigates a radio stripe aided ISAC system as a low-complexity implementation of a distributed system. We study the trade-off between achievable sum rate and sensing precision when downlink signals are used for target localization within the service area. By exploiting the architectural homogeneity of the radio stripes transceivers, each unit can be dynamically configured to operate in either communication or sensing mode. We formulate a targets localization problem considering the measurements of multiple sensing-communication configurations. Due to the large number of measurements and the continuity of the search space, we propose discretizing the service are and then solve the estimation problem in batches. The targets are finally estimated using a fusion strategy. Our results show that increasing the number devices and sensing APUs boosts sensing precision at the expense of degrading the sum rate. The latter remains constant for a given number of communication APUs regardless of their positions. Moreover, changing the number of antennas reveals a non-monotonic impact on sensing performance due to the trade-off between array gain and illumination uniformity.