End-to-End Learning for Symbol-Level Precoding and Detection with Adaptive Modulation

TL;DR

This paper introduces an end-to-end neural network framework that jointly optimizes modulation, precoding, and detection in symbol-level precoding systems, significantly enhancing communication performance.

Contribution

It proposes a novel neural network architecture that jointly optimizes modulation, precoding, and detection, overcoming limitations of fixed modulation schemes in SLP systems.

Findings

Notable performance improvements demonstrated in simulations.

Joint optimization outperforms traditional fixed modulation approaches.

Adaptive modulation enhances communication QoS.

Abstract

Conventional symbol-level precoding (SLP) designs assume fixed modulations and detection rules at the receivers for simplifying the transmit precoding optimizations, which greatly limits the flexibility of SLP and the communication quality-of-service (QoS). To overcome the performance bottleneck of these approaches, in this letter we propose an end-to-end learning based approach to jointly optimize the modulation orders, the transmit precoding and the receive detection for an SLP communication system. A neural network composed of the modulation order prediction (MOP-NN) module and the symbol-level precoding and detection (SLPD-NN) module is developed to solve this mathematically intractable problem. Simulations verify the notable performance improvement brought by the proposed end-to-end learning approach.