Naive Bayesian aided antenna selection for indoor NOMA GSSK VLC network

Abstract

This paper investigates the light-emitting diodes (LEDs) combinations selection problem in indoor non-orthogonal multiple access (NOMA) enabled generalized space shift keying (GSSK) multi-user visible light communications (VLC) network. To address the high computational complexity in the optimal LEDs combination selection problem, a naive Bayes (NB) aided LEDs combination selection algorithm is proposed to alleviate the high complexity while guaranteeing the system performance. Firstly, the LEDs combination selection process is modeled as a multi-classification problem; Then, by setting the key performance indicator (KPI) as the maximal sum-rate of multiple users, the generated training samples set is utilized to train a classification model by the NB approach; Finally, the classification model is applied to the LEDs combination selection of new users. Simulation results demonstrate that, compared with the optimal LEDs combination selection algorithm, the NB aided selection scheme proposed in this paper can effectively reduce the computational complexity and achieve a better balance between algorithm complexity and network transmission performance.