Adaptive CSI Feedback with Hidden Semantic Information Transfer

Abstract

Channel state information (CSI) feedback has been a challenging task for downlink frequency division duplex (FDD) system. Meanwhile, sensory data collection in large-scale network is pivotal to support intelligent applications in the central server. In this paper, we propose a deep-learning-empowered adaptive CSI feedback compression and quantization based on the information-bottleneck principle, where the sensory data transmission is hidden within the CSI feedback to eliminate extra communication cost and preserve the data privacy at the same time. To reduce the impact of information hiding on CSI feedback, we focus on hiding data in the semantic level. The tradeoffs among communication efficiency, CSI accuracy, hidden information transfer accuracy, and privacy are jointly optimized. Simulations further verify that the proposed scheme can achieve accurate sensory data collection without resource occupation and accurate CSI feedback with limited feedback overhead simultaneously.