用卷积神经网络联合优化智能反射面通信系统，性能超越理论极限。
A CNN-based End-to-End Learning for RIS-assisted Communication System
- 设计基于CNN的端到端自编码器，统一优化发射机、接收机与智能反射面
- 仿真显示系统误码率优于传统智能反射面通信的理论上限
- 适合研究智能反射面与深度学习融合的学者和工程师
可重构智能表面（RIS）是下一代移动通信中提升系统性能的新兴技术。本文提出一种基于卷积神经网络（CNN）的自编码器，用于联合优化RIS辅助通信系统的发射机、接收机及RIS。该系统协同优化编码/解码、信道估计、相位配置和调制/解调等子任务。数值结果表明，所提CNN自编码器系统的比特误码率（BER）性能优于传统RIS通信系统的理论BER上限。
原文摘要 · Abstract (English)
Reconfigurable intelligent surface (RIS) is an emerging technology that is used to improve the system performance in beyond 5G systems. In this letter, we propose a novel convolutional neural network (CNN)-based autoencoder to jointly optimize the transmitter, the receiver, and the RIS of a RIS-assisted communication system. The proposed system jointly optimizes the sub-tasks of the transmitter, the receiver, and the RIS such as encoding/decoding, channel estimation, phase optimization, and modulation/demodulation. Numerically we have shown that the bit error rate (BER) performance of the CNN-based autoencoder system is better than the theoretical BER performance of the RIS-assisted communication systems.
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