Movable Antenna-Aided Cooperative ISAC Network with Time Synchronization error and Imperfect CSI
Yue Xiu, Yang Zhao, Ran Yang, Dusit Niyato, Jing Jin, Qixing Wang,, Guangyi Liu, Ning Wei

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…
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Taxonomy
TopicsWireless Body Area Networks · Network Time Synchronization Technologies · Security in Wireless Sensor Networks
MethodsSpatio-temporal stability analysis
