Deep Learning based Efficient Symbol-Level Precoding Design for MU-MISO Systems

TL;DR

This paper introduces a deep learning approach with an efficient neural network to design symbol-level precoders in MU-MISO systems, significantly reducing computation time while maintaining near-optimal performance.

Contribution

It proposes a novel deep learning-based neural network method for symbol-level precoding that enhances efficiency and reduces computational complexity in MU-MISO systems.

Findings

Drastically reduces precoding computation time

Achieves near-optimal performance with slight loss

Validates effectiveness through simulations

Abstract

The recently emerged symbol-level precoding (SLP) technique has been regarded as a promising solution in multi-user wireless communication systems, since it can convert harmful multi-user interference (MUI) into beneficial signals for enhancing system performance. However, the tremendous computational complexity of conventional symbol-level precoding designs severely hinders the practical implementations. In order to tackle this difficulty, we propose a novel deep learning (DL) based approach to efficiently design the symbol-level precoders. Particularly, in this correspondence, we consider a multi-user multi-input single-output (MU-MISO) downlink system. An efficient precoding neural network (EPNN) is introduced to optimize the symbol-level precoders for maximizing the minimum quality-of-service (QoS) of all users under the power constraint. Simulation results demonstrate that the proposed EPNN based SLP design can dramatically reduce the computing time at the price of slight performance loss compared with the conventional convex optimization based SLP design.