P2P-Net: Position-Based Precoding for MIMO Downlink Transmission without CSI Feedback

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, where the precoder at the BS is directly derived from the location information of UE, without relying on channel measurement and CSI feedback. To achieve this, we devise a novel neural network (NN) structure called P2P-Net (Position-to-Precoder Net), which includes a position encoding module, an adaptive combination weight, and a refining module based on self-attention mechanism. With deep learning techniques, P2P-Net is able to learn the information about scatterers in the signal propagation environment, thereby realizing the mapping from position to precoder. Simulation results demonstrate the superiority of the proposed positionbased precoding method compared with existing feedback-based solutions in terms of spectral efficiency and communication overhead.