Cascaded Artificial Neural Networks for Proactive Power Allocation in Indoor LiFi Systems

Abstract

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity aimed for future sixth generation (6G) wireless networks. In the LiFi physical layer, the majority of the power allocation problems for mobile users investigated and reported in the literature are non-convex. These problems may be solved using dual decomposition techniques or heuristics that require iterative algorithms, and often, cannot be computed in real time due to the high computational load. In this paper, a proactive power allocation (PPA) approach that can alleviate the aforementioned issues is proposed. The core of the PPA approach is two cascaded neural networks consisting of one convolution neural network (CNN) and one long-short-term-memory (LSTM) network that are jointly capable of predicting posterior positions and orientations of mobile users following random trajectories in indoor environments. Afterwards, the predicted parameters are fed into the expression of the channel coefficients of the mobile users. Finally, the resulting predicted channel coefficients are exploited for deriving near-optimal power allocation schemes prior to the intended service time, which enables near-optimal and real-time service for mobile LiFi users.