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

arXiv: https://arxiv.org/abs/2501.15410 · HTML: https://mlsanity.com/paper/2501.15410/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2025.

## Built on by (most cited first, 5 of 5)

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- [SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification](https://mlsanity.com/paper/2601.12791/) (2026)
- [PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition](https://mlsanity.com/paper/2601.12798/) (2026)
- [JSR-GFNet: Jamming-to-Signal Ratio-Aware Dynamic Gating for Interference Classification in future Cognitive Global Navigation Satellite Systems](https://mlsanity.com/paper/2602.00042/) (2026)
- [Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection](https://mlsanity.com/paper/2604.05788/) (2026)

## Abstract (arXiv)

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.