Intelligent Omni-Surface-Aided Multi-Objective ISAC: A Meta Hybrid Deep Reinforcement Learning Approach

This paper studies an intelligent omni-surface (IOS)-aided integrated sensing and communication (ISAC) system, where a base station (BS) provides simultaneous target sensing and communication services with an IOS under outdated and imperfect channel state information (CSI). Both the communication sum-rate and sensing signal-to-noise ratio are maximized through joint optimization of BS beamforming and IOS configuration. To address this problem, we propose an intelligent joint optimization scheme called meta multi-objective hybrid deep reinforcement learning (meta-MHDRL). Specifically, the meta-MHDRL framework first introduces a hybrid deep reinforcement learning (DRL) approach that integrates double-critic-based deep deterministic policy gradient with deep double Q-network algorithms, enabling parallel optimization of both continuous-domain variables (i.e., BS beamforming, IOS reflecting phase shift, and IOS reflecting/refracting amplitudes) and the discrete-domain variable (i.e., IOS refracting phase shift). Thereafter, an objective-preference weight is incorporated into the hybrid DRL framework, such that meta-MHDRL can capture the trade-off between communication and sensing performance. To address the complex coupling relationships among different optimization variables, we further put forth a synchronized experience replay mechanism for meta-MHDRL, which maintains training synchronization among different neural networks. In addition, a meta-learning approach is developed to enhance the generalization ability of meta-MHDRL across different objective-preference weights. Simulation results show that meta-MHDRL attains more Pareto-efficient solutions than other schemes under outdated and imperfect CSI while maintaining stronger robustness across various simulation setups. Besides, we demonstrate the generalization ability of meta-MHDRL for unseen tasks © 2002-2012 IEEE.