Movable Antenna-Aided Cooperative ISAC Network with Time Synchronization error and Imperfect CSI

TL;DR

This paper proposes a movable antenna-based approach to enhance the robustness of cooperative ISAC networks against channel estimation and synchronization errors, using deep reinforcement learning for optimization.

Contribution

It introduces a novel MA-assisted framework with a hybrid Cramer-Rao bound analysis and a constrained deep reinforcement learning method for robust system design.

Findings

Significant robustness improvement under CSI and TS errors.

Optimized beamforming and MA placement reduce power consumption.

Deep RL approach effectively handles complex constraints.

Abstract

Cooperative-integrated sensing and communication (C-ISAC) networks have emerged as promising solutions for communication and target sensing. However, imperfect channel state information (CSI) estimation and time synchronization (TS) errors degrade performance, affecting communication and sensing accuracy. This paper addresses these challenges {by employing} {movable antennas} (MAs) to enhance C-ISAC robustness. We analyze the impact of CSI errors on achievable rates and introduce a hybrid Cramer-Rao lower bound (HCRLB) to evaluate the effect of TS errors on target localization accuracy. Based on these models, we derive the worst-case achievable rate and sensing precision under such errors. We optimize cooperative beamforming, {base station (BS)} selection factor and MA position to minimize power consumption while ensuring accuracy. {We then propose a} constrained deep reinforcement learning (C-DRL) approach to solve this non-convex optimization problem, using a modified deep deterministic policy gradient (DDPG) algorithm with a Wolpertinger architecture for efficient training under complex constraints. {Simulation results show that the proposed method significantly improves system robustness against CSI and TS errors, where robustness mean reliable data transmission under poor channel conditions.} These findings demonstrate the potential of MA technology to reduce power consumption in imperfect CSI and TS environments.