Meta-Reinforcement Learning Optimization for Movable Antenna-aided Full-Duplex CF-DFRC Systems with Carrier Frequency Offset

Yue Xiu; Wanting Lyu; You Li; Ran Yang; Phee Lep Yeoh; Wei Zhang; Guangyi Liu; Ning Wei

doi:10.48550/arxiv.2507.16132

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Meta-Reinforcement Learning Optimization for Movable Antenna-aided Full-Duplex CF-DFRC Systems with Carrier Frequency Offset

Yue Xiu, Wanting Lyu, You Li, Ran Yang, Phee Lep Yeoh, Wei Zhang, Guangyi Liu and Ning Wei

arXiv, Vol.22 July 2025

Cornell University

2025

DOI: https://doi.org/10.48550/arxiv.2507.16132

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Abstract

By enabling spectrum sharing between radar and communication operations, the cell-free dual-functional radar-communication (CF-DFRC) system is a promising candidate to significantly improve spectrum efficiency in future sixth-generation (6G) wireless networks. However, in wideband scenarios, synchronization errors caused by carrier frequency offset (CFO) can severely reduce both communication capacity and sensing accuracy. To address this challenge, this paper integrates movable antennas (MAs) into the CF-DFRC framework, leveraging their spatial flexibility and adaptive beamforming to dynamically mitigate CFO-induced impairments. To fully exploit the advantages of MAs in wideband scenarios with CFO, we aim to maximize the worst-case sum-rate of communication and sensing by jointly optimizing MA positions, {beamforming}, and CFO parameters, subject to transmit power and MA positioning constraints. Due to the non-convex nature of the problem, we propose a robust meta reinforcement learning (MRL)-based two-stage alternating optimization strategy. In the first stage, we employ manifold optimization (MO) with penalty dual decomposition (PDD) to solve the CFO-robust worst-case subproblem. In the second stage, we adopt to jointly optimize {the MA positions and beamforming vectors} in a data-driven manner {for dynamic wireless environments}. Simulation results show that the proposed MRL approach significantly outperforms conventional deep reinforcement learning (DRL) schemes in both communication and sensing performance under CFO impairments. Furthermore, compared to fixed-position antennas (FPAs), the MA-aided CF-DFRC system exhibits

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Title: Meta-Reinforcement Learning Optimization for Movable Antenna-aided Full-Duplex CF-DFRC Systems with Carrier Frequency Offset
Authors: Yue Xiu
Wanting Lyu
You Li
Ran Yang
Phee Lep Yeoh
Wei Zhang
Guangyi Liu
Ning Wei
Publication details: arXiv, Vol.22 July 2025
Publisher: Cornell University
Date published: 2025
DOI: 10.48550/arxiv.2507.16132
ISSN: 2331-8422
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 991198947202621
Output Type: Preprint

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