PS-Net: Position-Based Precoding With Sensing Assistance for MIMO Downlink Transmission

Abstract

In frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) communication systems, the base station (BS) requires channel state information (CSI) reported from user equipment (UE) for downlink precoding, which brings in significant feedback overhead. In this paper, we propose a position-based precoding method with sensing assistance to realize MIMO downlink transmission without CSI feedback from UE. By exploiting the location of UE and the information of the propagation environment provided by wireless sensing techniques, the BS is able to derive the precoder for downlink transmission. To achieve this, we devise a novel neural network (NN) structure called PS-Net (Position-based-precoding with Sensing-assistance Network), which includes an environmental feature extractor, a weight generation module, an adaptive position encoder, and a position-to-precoder mapper. Using the PS-Net, information about the scatters in the propagation environment can be extracted and fused with the UE’s location to realize position-based precoding for time-varying channels. We also propose a dedicated data augmentation method called random phase shifting to enhance the training data diversity, thus improving the generalization ability of PS-Net. Simulation results demonstrate the superiority of the proposed PS-Net compared with the existing feedback-based solutions and other position-based approaches in terms of spectral efficiency and communication overhead.