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光纤无线融合智能传输系统研究进展
Research Progress on Fiber Wireless Integrated Intelligent Transmission Systems
- 光通信研究 2025年第5期
- 作者机构：
1.东南大学 移动通信全国重点实验室，南京 210096
2.紫金山实验室，南京 211111
- 作者简介：
毛军开（2002-），男，江西上饶人。硕士，主要研究方向为智能通信。
张教，副研究员。E-mail：zhangjiao1@pmlabs.com.cn
- 基金信息：国家重点研发计划资助项目(2024YFE0200202);国家自然科学基金资助项目(62435003;62271135;62405384;62201397;62201393);江苏省重点研发计划资助项目(BE2023001)
DOI：10.13756/j.gtxyj.2025.250261
中图分类号： TN929收稿：2025-07-25，
修回：2025-08-19，
纸质出版：2025-10-10
- 稿件说明：
移动端阅览
毛军开，张教，朱敏. 光纤无线融合智能传输系统研究进展[J]. 光通信研究，2025(5): 250261.
Mao J K, Zhang J, Zhu M. Research Progress on Fiber Wireless Integrated Intelligent Transmission Systems[J]. Study on Optical Communications, 2025(5): 250261.
毛军开，张教，朱敏. 光纤无线融合智能传输系统研究进展[J]. 光通信研究，2025(5): 250261. DOI： 10.13756/j.gtxyj.2025.250261.
Mao J K, Zhang J, Zhu M. Research Progress on Fiber Wireless Integrated Intelligent Transmission Systems[J]. Study on Optical Communications, 2025(5): 250261. DOI： 10.13756/j.gtxyj.2025.250261.
摘要
太赫兹通信技术作为第6代移动通信（6G）的核心技术，可以实现更大的传输带宽，有效解决频谱资源稀缺的问题，以满足流量和连接数急剧增长的需求。光纤无线融合（FiWi）通信系统因其高带宽、低时延和灵活覆盖的优势，已成为实现6G太赫兹通信与全场景连接的关键技术。然而，FiWi系统依旧存在线性和非线性损伤、码间干扰（ISI）和相位噪声等一些关键技术挑战。近年来，依靠机器学习（ML）技术强大的数据和模型驱动建模能力的智能信号处理，成了FiWi系统优化的新途径。文章根据ML在FiWi系统的应用，分别从信道模型、后均衡和端到端（E2E）处理3个部分，介绍了较为经典的或是最新的应用于FiWi中的ML技术，并对未来FiWi智能通信系统进行了展望。
Abstract
Terahertz communication technology is at the heart of The 6th Generation Network (6G). It can increase the transmission bandwidth and solve the problem of limited spectrum resources
making it possible to keep up with the fast growth of traffic and contacts. Due to its high bandwidth
low latency
and variable coverage
the Fiber Wireless Integrated (FiWi) communication system is now an important part of 6G terahertz communication and full-scenario connection. However
the FiWi system still has to deal with some major technical problems
such as linear and nonlinear problems
Inter Symbol Interference (ISI)
phase noise
and etc. Smart signal processing using the strong data and model-driven modeling features of Machine Learning (ML) techniques has emerged as a new way to improve FiWi systems in recent years. Considering the application of ML in the FiWi system
this paper introduces classical and current ML technologies that are used in FiWi in three parts: channel model
post-equalization
and End to End (E2E) processing. It also looks forward to the future FiWi intelligent communication system.
关键词
Keywords
references
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