Sparse I/Q-joint DNN nonlinear equalization based on progressive pruning for a photonics-aided 256-QAM MMW communication system

Abstract

An efficient nonlinear equalizer based on the pruning I/Q-joint deep neural network (DNN) is proposed and experimentally demonstrated to mitigate the nonlinearity in a photonics-assisted millimeter-wave (MMW) system with a high-order 256 quadrature-amplitude-modulation (QAM) format. Experimental findings reveal that implementing pruning on the I/Q-joint DNN can compress the computational overhead by 32% while accommodating 256-QAM E-band MMW transmission for a net throughput of 66.67 Gbps with 20.21% less complexity than the traditional Volterra nonlinear equalizer. Compared with the I/Q dual DNN with the same complexity, a 16% pruning ratio improvement is enabled by a robust pruning I/Q-joint DNN that further deciphers the I/Q relationship.