SVM-aided Signal Detection in Generalized Space Shift Keying Visible Light Communication System

Novel signal detection technique is conceived for Generalized Space Shift Keying (GSSK) modulated indoor Visible Light Communication (VLC) system, which is aided by one of popular machine learning approach termed as Support Vector Machine (SVM). For general classic VLC system, as the optimal detection algorithm, Maximum Likelihood (ML) detection has a high computational complexity. In order to alleviate this problem, classification idea in SVM is utilized to realize signal detection at the user's receiving end by a particular trained learning model. As a result, a signal detection algorithm for the considered GSSK-VLC system based on SVM is designed with lower computational complexity and nearly optimal detection accuracy. Simulation results demonstrate that the proposed SVM-aided signal detection technique can have near optima ML Bit Error Rate (BER) performance while the computational complexity is significantly reduced in the considered indoor GSSK-VLC system