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

Rang Liu; Zhu Bo; Ming Li; Qian Liu

doi:10.1109/lwc.2022.3216848

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arXiv · 2210.13744

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

Rang Liu·Zhu Bo·Ming Li·Qian Liu

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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.

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BibTeX RIS

Rang Liu, Zhu Bo, Ming Li, Qian Liu. 2022-10-25. End-to-End Learning for Symbol-Level Precoding and Detection with Adaptive Modulation. https://doi.org/10.1109/lwc.2022.3216848

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