Work

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

Year: 2022

Type: article

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.

Source: IEEE Wireless Communications Letters

DOI: 10.1109/lwc.2022.3216848

Authors: Rang Liu, Zhu Bo, Ming Li, Qian Liu

Institution: Dalian University of Technology

Language: English

Topic: Advanced Wireless Communication Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities, Dalian Science and Technology Innovation Fund