  This paper investigates the channel aging problem of mobile light-fidelity
(LiFi) systems. In the LiFi physical layer, the majority of the optimization
problems for mobile users are non-convex and require the use of dual
decomposition or heuristics techniques. Such techniques are based on iterative
algorithms, and often, cause a high processing delay at the physical layer.
Hence, the obtained solutions are no longer optimal since the LiFi channels are
evolving. In this paper, a proactive-optimization (PO) approach that can
alleviate the LiFi channel aging problem is proposed. The core idea is to
design a long-short-term-memory (LSTM) network that is capable of predicting
posterior positions and orientations of mobile users, which can be then used to
predict their channel coefficients. Consequently, the obtained channel
coefficients can be exploited to derive near-optimal transmission-schemes prior
to the intended service-time, which enables real-time service. Through various
simulations, the performance of the designed LSTM model is evaluated in terms
of prediction error and time, as well as its application in a practical LiFi
optimization problem.
