Support Vector Machine-Based Soft Decision for Consecutive-Symbol-Expanded 4-Dimensional Constellation in Underwater Visible Light Communication System

Abstract

Nowadays, underwater visible light communication (UVLC) has become one of the key technologies for high-speed underwater wireless communication. Because of the limited modulation bandwidth and nonlinearity of the optoelectronic devices in the UVLC system, the combination of inter-symbol interference and nonlinear impairment will inevitably degrade the transmission performance. Advanced digital signal processing methods including equalization and decoding are required. In the past few years, Support vector machine (SVM) has been widely investigated in quadrature amplitude modulation (QAM) for soft decision in the decoding process. However, previous works only consider 2-dimensional (2-D) separate symbol, ignoring the correlation between consecutive symbols. In this paper, we propose to use SVM for soft decision with a 4-dimensional (4-D) constellation by concatenating two consecutive symbols. To deal with the increasing computational complexity in the SVM training phase, bit-based binary SVM multi-class strategy and an edge-detection-based data pre-processing method are employed. In this paper, we demonstrate a carrierless amplitude and phase (CAP) 16-QAM UVLC system. Experimental results indicate that the performance is greatly improved when using consecutive-symbol-expanded 4-D constellation with SVM for soft decision.

Adaptive Fuzzy Logic Deep-Learning Equalizer for Mitigating Linear and Nonlinear Distortions in Underwater Visible Light Communication Systems

“…Niu et al [ 15 ] advocated for using a support vector machine (SVM), which has been extensively studied in quadruple frequency modulation for decryption to make softer decisions, manage to increase computational complexity, and eliminate distortion. Li et al [ 16 ] suggested using a gated recurrent unit (GRU) neural network-driven equalization to compensate for linear and nonlinear distortions in carrier-less amplitude-phase band-constrained UVLC systems.…”

“…I/Q components, which stand for in-phase and quadrature, are often concatenated. The kernel function can transform information from a “lower-dimensional feature space to a higher-dimensional feature space”, allowing for linear separation of the training data set if it cannot be linearly separated [ 15 ].…”