Optimization of Delta-Sigma Modulator Based on Reinforcement Learning for Mobile Fronthaul

被引：0

作者：

Yan, Zijun ^{[1
]}

Zhu, Yixiao ^{[1
]}

Yang, Guangying ^{[1
]}

Hu, Weisheng ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Dept Elect Engn, State Key Lab Adv Opt Commun Syst & Networks, Shanghai 200240, Peoples R China

来源：

IEEE PHOTONICS TECHNOLOGY LETTERS | 2025年 / 37卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Signal to noise ratio; Training; Optimization; Nonlinear filters; Maximum likelihood detection; Trajectory; Poles and zeros; Filtering algorithms; Reinforcement learning; Optical filters; Delta-sigma modulator; mobile fronthaul; reinforcement learning; optimization;

D O I：

10.1109/LPT.2025.3546995

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Delta-sigma modulator (DSM) offers a high-fidelity solution for mobile fronthaul. It achieves a high quantization signal-to-noise ratio through oversampling and noise shaping. However, the DSM is a nonlinear system that lacks of comprehensive design method for the loop filter. The traditional loop filter design methods established on linear filter systems have suboptimal performance. In this work, we propose a reinforcement learning (RL)-based method to optimize the design of the loop filter, which utilizes the historical information. The RL method achieves a 14-dB and a >2.4-dB SNR gain compared to traditional methods and polynomial fitting-based approach, respectively.

引用

页码：397 / 400

页数：4

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