Partial pruning strategy for a dual-branch multilayer perceptron-based post-equalizer in underwater visible light communication systems.

Abstract

We demonstrate a partial pruning strategy for a post-equalizer based on a dual-branch multilayer perceptron-based post-equalizer (DBMLP PE) in an underwater visible light communication (UVLC) system. The partial pruning strategy produces a sparse DBMLP PE (SDBMLP PE) with less space complexity than the Volterra PE and bit error rate (BER) performance similar to the DBMLP PE. We experimentally prove the effectiveness and necessity of the partial pruning algorithm in multilayer perceptron-based (MLP) PE. The partial pruning strategy consists of two parts: (i) preventing the pruning of connections to the output nodes, and (ii) avoiding the linear mapping branches of the DBMLP during the pruning process. Our experiments prove that the SDBMLP PE further reduces the BER of the UVLC system by 36.5%, with only 33.8% parameters of the Volterra PE. To the best of our knowledge, this paper presents the first deep neural network-based PE with lower complexity and higher BER performance than the Volterra PE in the UVLC system, which dramatically increases the applicability of artificial neural network-based (ANN) PE in the field of UVLC systems.