Multi-Channel Nonlinearity Mitigation Using Machine Learning Algorithms
Multi-Channel Nonlinearity Mitigation Using Machine Learning Algorithms
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DOI：
10.1109/tmc.2023.3259880
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发表时间：
2024-04
影响因子：
7.9
通讯作者：
Haotian Zhao;Julian Camilo Gomez Diaz;S. Hoyos
中科院分区：
文献类型：
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作者：
Haotian Zhao;Julian Camilo Gomez Diaz;S. Hoyos
本文研究了在接收机非线性和噪声存在的情况下的多通道机器学习(ML)技术，并将结果与单通道接收机结构进行了比较。众所周知，多通道结构放宽了模数转换的采样速度要求，并且由于这些接收器固有的带宽分割特性，对时钟抖动和前端噪声提供了显著的稳健性。然而，当高压摆幅信号用于有线通信链路时，接收信号受到模拟前端(AFE)的非线性轮廓引起的三次谐波失真和互调产物的影响。为此，本文提出了结合非线性反馈抵消的信道判决传递(CDP)算法作为一种低复杂度的非线性抑制方案，并与其他著名的最大似然算法进行了性能比较。仿真结果表明，在实际的非线性分布和噪声条件下，采用非线性反馈抵消和CDP的多通道接收机结构与单通道结构相比有明显的改善。
This paper investigates multi-channel machine learning (ML) techniques in the presence of receiver nonlinearities and noise, and compares the results with the single-channel receiver architecture. It is known that the multi-channel architecture relaxes the sampling speed requirement of analog to digital conversion and provides significant robustness to clock jitter and front-end noise due to the bandwidth-splitting property inherent in these receivers. However, when a high-voltage swing signal is used in a wireline communication link, the received signal suffers from third-order harmonic distortions and inter-modulation products caused by the nonlinearity profile of the analog front-end (AFE). To this end, this paper proposes the channel decision passing (CDP) algorithm in combination with nonlinear feedback cancellation as a low-complexity candidate for nonlinearity mitigation and compares the performance of this solution with other well-known ML algorithms. Simulation results show significant improvement in a multi-channel receiver architecture equipped with nonlinear feedback cancellation and CDP in comparison with its single-channel counterpart under practical nonlinearity profiles and noise conditions.