CSI Acquisition in Internet of Vehicle Network: Federated Edge Learning With Model Pruning and Vector Quantization

Abstract

The conventional machine learning (ML)–based channel state information (CSI) acquisition has overlooked the potential privacy disclosure and estimation overhead problem caused by transmitting pilot datasets during the estimation stage. In this paper, we propose federated edge learning for CSI acquisition to protect the data privacy in the Internet of vehicle network with massive antenna array. To reduce the channel estimation overhead, the joint model pruning and vector quantization algorithm for network gradient parameters is presented to reduce the amount of exchange information between the centralized server and devices. This scheme allows for local fine-tuning to adapt the global model to the channel characteristics of each device. In addition, we also provide theoretical guarantees of convergence and quantization error bound in closed form, respectively. Simulation results demonstrate that the proposed FL-based CSI acquisition with model pruning and vector quantization scheme can efficiently improve the performance of channel estimation while reducing the communication overhead.