  This paper investigates the channel aging problem of light-fidelity (LiFi)
systems. In the LiFi physical layer, the majority of the optimization problems
for mobile users are nonconvex and require the use of dual decomposition or
heuristics techniques. Such techniques are based on iterative algorithms, and
often, cause a high processing time at the physical layer. Hence, the obtained
solutions are no longer optimal since the LiFi channels are evolving. In this
paper, a proactive-optimization 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
for deriving 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
accuracy and time. Finally, the performance of the proposed PO approach is
investigated in the sum rate maximization problem of multiuser cell-free LiFi
systems with quality-of-service constraints, where a performance gap of less
than 7% is achieved, while eliminating up to 100% of the online
processing-time.
