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1.北京大学光子传输与通信全国重点实验室,北京 100871
2.中国电信股份有限公司研究院光纤光缆先进制造与 应用技术全国重点实验室,北京 100032
[ "秦昊(2001- ),男,北京大学电子学院博士生,主要研究方向为高速光通信与光互连。" ]
[ "杨川川(1982- ),女,博士,北京大学电子学院副教授,主要研究方向为高速光传输、光互连与光传感。" ]
[ "蓝天祥(1998- ),男,北京大学电子学院博士生,主要研究方向为高速光通信与光互连。" ]
[ "秦忻(1995- ),女,博士,现就职于中国电信股份有限公司研究院,主要研究方向为智能光网络与数字孪生光网络。" ]
[ "赵玉萍(1961- ),女,博士,北京大学电子学院教授,主要研究方向为无线通信系统。" ]
[ "陈章渊(1968- ),男,博士,北京大学电子学院教授,区域光纤通信网与新型光通信系统国家重点实验室主任,主要研究方向为光传输、微波光子学和光子器件。" ]
收稿日期:2025-02-18,
修回日期:2025-04-02,
纸质出版日期:2025-04-20
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秦昊,杨川川,蓝天祥等.超高速短距离光互连信号均衡及编码技术[J].电信科学,2025,41(04):20-31.
QIN Hao,YANG Chuanchuan,LAN Tianxiang,et al.Signal equalization and coding techniques in ultra-high-speed short-reach optical interconnect[J].Telecommunications Science,2025,41(04):20-31.
秦昊,杨川川,蓝天祥等.超高速短距离光互连信号均衡及编码技术[J].电信科学,2025,41(04):20-31. DOI: 10.11959/j.issn.1000-0801.2025103.
QIN Hao,YANG Chuanchuan,LAN Tianxiang,et al.Signal equalization and coding techniques in ultra-high-speed short-reach optical interconnect[J].Telecommunications Science,2025,41(04):20-31. DOI: 10.11959/j.issn.1000-0801.2025103.
在人工智能(artificial intelligence,AI)大模型蓬勃发展的背景下,数据中心光模块速率需求持续攀升。为满足超高速短距离光互连高速率、低能耗、低时延、高集成度的要求,介绍了光模块产业发展趋势,并着重探讨相关均衡和编码技术。在均衡技术方面,介绍了基于最小角回归策略的低复杂度沃特均衡方法LaNLE和基于隐藏特征提取(hidden feature extraction,HFE)的深度神经网络均衡方法。LaNLE在120 Gbit/s PAM-8信号100 m传输下,相同误码率时,复杂度相比传统方法降低了70.1%;HFE可提升神经网络训练效率,实现了288 Gbit/s PAM-8信号传输。在编码技术方面,介绍了光互连系统中QC-LDPC码优化方法和基于深度学习的LDPC联合均衡译码方法,经实验验证,所提方法能有效优化误码率,提升系统性能。同时,对未来研究方向进行了展望。
Against the backdrop of the vigorous development of artificial intelligence (AI) large models
the demand for the data center optical module rate continues to rise. To meet the requirements of high rate
low power consumption
low latency
and high integration for ultra-high-speed short-reach optical interconnect
the development trend of the optical module industry was introduced
and relevant equalization and coding technologies were discussed. In terms of equalization technology
a low-complexity Volterra equalization method based on the least angle regression strategy (LaNLE) and a deep neural network equalization method based on hidden feature extraction (HFE) were proposed. Under the 120 Gbit/s PAM-8 signal transmission
the complexity of LaNLE was reduced by 70.1% compared with traditional methods at the same bit error rate; HFE improved the neural network training efficiency and achieved 288 Gbit/s PAM-8 signal transmission. In terms of coding technology
the optimization method of QC-LDPC codes in the optical interconnection system and the LDPC joint equalization and decoding method based on deep learning were introduced. Experimental results show that the proposed methods can effectively optimize the bit error rate and improve the system performance. At the same time
the future research directions were prospected.
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