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.
@article{arxiv.2210.13744,
title = {End-to-End Learning for Symbol-Level Precoding and Detection with Adaptive Modulation},
author = {Rang Liu and Zhu Bo and Ming Li and Qian Liu},
journal= {arXiv preprint arXiv:2210.13744},
year = {2023}
}