Learning-Based Intermittent CSI Estimation With Adaptive Intervals in Integrated Sensing and Communication Systems
Jie Chen, Xianbin Wang · IEEE Journal of Selected Topics in Signal Processing · 2024
Due to the distinct objectives and multipath utilization mechanisms between the communication and radar modules, the system design of integrated sensing and communication (ISAC) necessitates two types of channel state information (CSI), i.e., communication CSI representing the whole channel gain and phase shifts, and radar CSI exclusively focused on target mobility and position information. However, current ISAC systems apply an identical mechanism to estimate both types of CSI at the same predetermined estimation interval based on the worst case of dynamic environments, leading to significant overhead and compromised performances. Therefore, this paper proposes an intermittent communication and radar CSI estimation scheme with adaptive intervals for individual users/targets, where both types of CSI can be predicted using channel temporal correlations for cost reduction or re-estimated via signal transceiving for improved estimation accuracy. Specifically, we jointly optimize the binary CSI re-estimation/prediction decisions and transmit beamforming matrices for individual users/targets to maximize communication transmission rates and minimize radar tracking errors and costs in a multiple-input single-output (MISO) ISAC system. Unfortunately, this problem has causality issues because it requires comparing system performances under re-estimated CSI and predicted CSI during the optimization. However, the re-estimated CSI can only be obtained after completing the optimization. Additionally, the binary decision makes the joint design a mixed integer nonlinear programming (MINLP) problem, resulting in high complexity when using conventional optimization algorithms. Therefore, we propose a deep reinforcement online learning (DROL) framework that first implements an online deep neural network (DNN) to learn the binary CSI updating policy from the experiences. Given the learned policy, we propose an efficient algorithm to solve the remaining beamforming design problem. Finally, simulation results validate the effectiveness of the proposed algorithm.