Channel Equalization Through Reservoir Computing: A Theoretical Perspective
IEEE Wireless Communications Letters(2023)
摘要
Deep learning practice, including in wireless communications, often relies on trial and error to optimize neural network (NN) structures and their corresponding hyperparameters. We show that Reservoir Computing, especially the Echo State Network (ESN), is an ideal learning-based equalizer for a general fading channel and for an ESN equalizing a channel with known statistics, theoretically derive its optimum reservoir weights which are randomly initialized in state-of-the-art and lack interpretability. The theoretical results are validated with simulations. In contrast to existing literature, this letter analytically adapts the NN structure to the problem being addressed, guaranteeing optimum equalization under known channel statistics.
更多查看译文
关键词
Reservoir computing,echo state network,neural network,channel equalization,and receive processing
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要