Liu Cuiwei, Wang Chen, Zhou Wen, Wang Feng, Kong Miao, Yu Jianjun
Opt Express. 2022 Jan 17;30(2):2364-2377. doi: 10.1364/OE.448845.
We propose a dual gated recurrent unit neural network based on nonlinear equalizer (dual-GRU NLE) for radio-over-fiber (ROF) communication systems. The dual equalization scheme is mainly based upon GRU algorithm, which can be trained via two steps including I-GRU and Q-GRU. By using the dual-GRU equalizer, 60-Gbps 64-QAM signal generation and transmission over 10-km SMF and 1.2-m wireless link at 81-GHz can be achieved. For the digital signal processing (DSP) at receiver, comparison between CMMA equalizer, Volterra equalizer, and dual-GRU equalizer are demonstrated. The results indicate that the proposed dual-GRU NLE significantly mitigates the nonlinear distortions. The dual-GRU equalizer has a better BER performance in receiver sensitivity than the traditional CMMA and Volterra equalizer. At the expense of large complexity, an improvement of receiver sensitivity can be achieved as much as 1 dB compared with Volterra equalizer at the BER of 2×10. To the best of our knowledge, this is the first time to propose a novel dual-GRU equalizer, which is promising for the development in millimeter-wave photonics for B5G applications and beyond.
我们提出了一种基于非线性均衡器的双门控循环单元神经网络(dual-GRU NLE),用于光纤无线(ROF)通信系统。双均衡方案主要基于GRU算法,该算法可通过包括I-GRU和Q-GRU的两个步骤进行训练。通过使用双GRU均衡器,可以实现60 Gbps 64-QAM信号在10公里单模光纤和81 GHz下1.2米无线链路上的生成和传输。对于接收机处的数字信号处理(DSP),展示了CMMA均衡器、Volterra均衡器和双GRU均衡器之间的比较。结果表明,所提出的双GRU NLE显著减轻了非线性失真。双GRU均衡器在接收机灵敏度方面比传统的CMMA和Volterra均衡器具有更好的误码率性能。以较大的复杂度为代价,在误码率为2×10时,与Volterra均衡器相比,接收机灵敏度可提高多达1 dB。据我们所知,这是首次提出一种新型双GRU均衡器,它对于5G及以后的毫米波光子学发展具有广阔前景。