Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example
Abstract
Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method which utilizes wireless environmental information with pilot pattern optimization for CSI prediction (WEI-CSIP). Specifically, scatterers around the mobile station (MS) are abstracted from environmental information using multiview images. Then, an environmental feature map is extracted by a convolutional neural network (CNN). Additionally, the deep probabilistic subsampling (DPS) network acquires an optimal fixed pilot pattern. Finally, a CNN-based channel prediction network is designed to predict the complete CSI, using the environmental feature map and partial CSI. Simulation results show that the WEI-CSIP can reduce pilot overhead from 1/5 to 1/8, while improving prediction accuracy with normalized mean squared error reduced to 0.0113, an improvement of 83.2% compared to traditional channel prediction methods.
Keywords
Cite
@article{arxiv.2408.06558,
title = {Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example},
author = {Lianzheng Shi and Jianhua Zhang and Li Yu and Yuxiang Zhang and Zhen Zhang and Yichen Cai and Guangyi Liu},
journal= {arXiv preprint arXiv:2408.06558},
year = {2024}
}