Abstract
In this article, a novel scheme to effectively mitigate the nonlinear impairments in a PAM-8 radio-over-fiber (ROF) delivery is proposed by a joint deep neuron network (J-DNN) equalizer, which has more superiority in terms of good training accuracy, satisfactory tracking speed, and over-fitting suppression compared with a typical deep neuron network (DNN) equalizer. Our proposed J-DNN equalization scheme is mainly based upon back-propagation (BP) algorithm and blind cascaded multi-modulus algorithm (CMMA), which can be trained via two steps including DNN initialization and DNN optimization. By using the proposed J-DNN equalizer, 60-Gbps PAM-8 signal generation and transmission over 10-km SMF and 3-m wireless link at 135-GHz can be achieved. For the digital signal processing (DSP) at receiver, comparisons between CMMA equalizer, DNN equalizer, and J-DNN equalizer are demonstrated. The results indicate that J-DNN equalizer has a much better BER performance in receiver sensitivity than the traditional CMMA, and an improvement of receiver sensitivity can be achieved as much as 1 dB compared with a DNN equalizer at the BER of 3.8 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . To the best of our knowledge, this is the first time to propose a novel joint DNN equalizer, which is promising for the development in integrated microwave photonics and microwave/millimeter-wave photonics for 5G applications and beyond.
Published Version
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