Abstract
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 2×10−2. 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.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Subject
Atomic and Molecular Physics, and Optics
Cited by
11 articles.
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