LAUSR

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.