Multi-Static ISAC based on Network-Assisted Full-Duplex Cell-Free Networks: Performance Analysis and Duplex Mode Optimization

TL;DR

This paper introduces a multi-static ISAC system based on network-assisted full-duplex cell-free networks, optimizing duplex modes to enhance communication and sensing performance with a novel deep reinforcement learning approach.

Contribution

It proposes a new multi-static ISAC design leveraging NAFD cell-free networks and develops a deep Q-network-based algorithm for duplex mode optimization.

Findings

Significantly improved communication performance over existing ISAC systems.

Accurate closed-form expressions for communication rate and localization error.

Optimization algorithm achieves near-exhaustive performance with low complexity.

Abstract

Multi-static integrated sensing and communication (ISAC) technology, which can achieve a wider coverage range and avoid self-interference, is an important trend for the future development of ISAC. Existing multi-static ISAC designs are unable to support the asymmetric uplink (UL)/downlink (DL) communication requirements in the scenario while simultaneously achieving optimal sensing performance. This paper proposes a design for multi-static ISAC based on network-assisted full-duplex (NAFD) cell-free networks can well solve the above problems. Under this design, closed-form expressions for the individual comunication rate and localization error rate are derived under imperfect channel state information, which are respectively utilized to assess the communication and sensing performances. Then, we propose a deep Q-network-based accesss point (AP) duplex mode optimization algorithm to obtain the trade-off between communication and sensing from the UL and DL perspectives of the APs. Simulation results demonstrate that the NAFD-based ISAC system proposed in this paper can achieve significantly better communication performance than other ISAC systems while ensuring minimal impact on sensing performance. Then, we validate the accuracy of the derived closed-form expressions. Furthermore, the proposed optimization algorithm achieves performance comparable to that of the exhaustion method with low complexity.