Robust Optimization for Movable Antenna-aided Cell-Free ISAC with Time Synchronization Errors

TL;DR

This paper introduces a robust movable antenna architecture for cell-free ISAC systems that mitigates time synchronization errors, enhances sensing accuracy, and increases system capacity through joint optimization and advanced learning techniques.

Contribution

It proposes a novel movable antenna-based CF-ISAC system with a robust optimization framework addressing time synchronization errors, using manifold optimization and meta-reinforcement learning.

Findings

Significant improvement in sensing accuracy under TS errors.

Enhanced system capacity compared to fixed antenna systems.

Robust algorithms effectively mitigate synchronization issues.

Abstract

The cell-free integrated sensing and communication (CF-ISAC) system, which effectively mitigates intra-cell interference and provides precise sensing accuracy, is a promising technology for future 6G networks. However, to fully capitalize on the potential of CF-ISAC, accurate time synchronization (TS) between access points (APs) is critical. Due to the limitations of current synchronization technologies, TS errors have become a significant challenge in the development of the CF-ISAC system. In this paper, we propose a novel CF-ISAC architecture based on movable antennas (MAs), which exploits spatial diversity to enhance communication rates, maintain sensing accuracy, and reduce the impact of TS errors. We formulate a worst-case sensing accuracy optimization problem for TS errors to address this challenge, deriving the worst-case Cram\'er-Rao lower bound (CRLB). Subsequently, we develop a joint optimization framework for AP beamforming and MA positions to satisfy communication rate constraints while improving sensing accuracy. A robust optimization framework is designed for the highly complex and non-convex problem. Specifically, we employ manifold optimization (MO) to solve the worst-case sensing accuracy optimization problem. Then, we propose an MA-enabled meta-reinforcement learning (MA-MetaRL) to design optimization variables while satisfying constraints on MA positions, communication rate, and transmit power, thereby improving sensing accuracy. The simulation results demonstrate that the proposed robust optimization algorithm significantly improves the accuracy of the detection and is strong against TS errors. Moreover, compared to conventional fixed position antenna (FPA) technologies, the proposed MA-aided CF-ISAC architecture achieves higher system capacity, thus validating its effectiveness.