Jiang Yachen, Xu Sicong, Wang Qihang, Zhang Jie, Ge Jingtao, Lin Jingwen, Ma Yuan, Wang Siqi, Ou Zhihang, Zhou Wen
Key Laboratory for Information Science of Electromagnetic Waves (MoE), Shanghai Institute for Advanced Communication and Data Science, Fudan University, Shanghai 200433, China.
Sensors (Basel). 2025 Jun 11;25(12):3661. doi: 10.3390/s25123661.
High demand for 6G wireless has made photonics-aided D-band (110-170 GHz) communication a research priority. Photonics-aided technology integrates optical and wireless communications to boost spectral efficiency and transmission distance. This study presents a Radio-over-Fiber (RoF) communication system utilizing photonics-aided technology for 4600 m long-distance D-band transmission. We successfully show the transmission of a 50 Gbps (25 Gbaud) QPSK signal utilizing a 128.75 GHz carrier frequency. Notwithstanding these encouraging outcomes, RoF systems encounter considerable obstacles, including pronounced nonlinear distortions and phase noise related to laser linewidth. Numerous factors can induce nonlinear impairments, including high-power amplifiers (PAs) in wireless channels, the operational mechanisms of optoelectronic devices (such as electrical amplifiers, modulators, and photodiodes), and elevated optical power levels during fiber transmission. Phase noise (PN) is generated by laser linewidth. Despite the notable advantages of classical Volterra series and deep neural network (DNN) methods in alleviating nonlinear distortion, they display considerable performance limitations in adjusting for phase noise. To address these problems, we propose a novel post-processing approach utilizing a two-dimensional convolutional neural network (2D-CNN). This methodology allows for the extraction of intricate features from data preprocessed using traditional Digital Signal Processing (DSP) techniques, enabling concurrent compensation for phase noise and nonlinear distortions. The 4600 m long-distance D-band transmission experiment demonstrated that the proposed 2D-CNN post-processing method achieved a Bit Error Rate (BER) of 5.3 × 10 at 8 dBm optical power, satisfying the soft-decision forward error correction (SD-FEC) criterion of 1.56 × 10 with a 15% overhead. The 2D-CNN outperformed Volterra series and deep neural network approaches in long-haul D-band RoF systems by compensating for phase noise and nonlinear distortions via spatiotemporal feature integration, hierarchical feature extraction, and nonlinear modelling.
对6G无线的高需求使光子辅助D波段（110 - 170GHz）通信成为研究重点。光子辅助技术将光通信和无线通信集成在一起，以提高频谱效率和传输距离。本研究提出了一种利用光子辅助技术进行4600米长距离D波段传输的光纤无线（RoF）通信系统。我们成功展示了利用128.75GHz载波频率传输50Gbps（25Gbaud）QPSK信号。尽管有这些令人鼓舞的成果，但RoF系统仍面临相当大的障碍，包括与激光线宽相关的明显非线性失真和相位噪声。许多因素会引起非线性损伤，包括无线信道中的高功率放大器（PA）、光电器件（如电放大器、调制器和光电二极管）的工作机制以及光纤传输过程中升高的光功率水平。相位噪声（PN）由激光线宽产生。尽管经典的沃尔泰拉级数和深度神经网络（DNN）方法在减轻非线性失真方面有显著优势，但它们在调整相位噪声方面表现出相当大的性能局限性。为了解决这些问题，我们提出了一种利用二维卷积神经网络（2D - CNN）的新型后处理方法。这种方法允许从使用传统数字信号处理（DSP）技术预处理的数据中提取复杂特征，从而能够同时补偿相位噪声和非线性失真。4600米长距离D波段传输实验表明，所提出的2D - CNN后处理方法在8dBm光功率下实现了5.3×10的误码率（BER），以15%的开销满足了1.56×10的软判决前向纠错（SD - FEC）标准。在长距离D波段RoF系统中，2D - CNN通过时空特征集成、分层特征提取和非线性建模来补偿相位噪声和非线性失真，其性能优于沃尔泰拉级数和深度神经网络方法。
