Resource and Mobility Management in Hybrid LiFi and WiFi Networks: A
  User-Centric Learning Approach
Han Ji Xiping Wu
Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets)
are an emerging indoor wireless communication paradigm, which combines the
advantages of the capacious optical spectra of LiFi and ubiquitous coverage of
WiFi. Meanwhile, load balancing (LB) becomes a key challenge in resource
management for such hybrid networks. The existing LB methods are mostly
network-centric, relying on a central unit to make a solution for the users all
at once. Consequently, the solution needs to be updated for all users at the
same pace, regardless of their moving status. This would affect the network
performance in two aspects: i) when the update frequency is low, it would
compromise the connectivity of fast-moving users; ii) when the update frequency
is high, it would cause unnecessary handovers as well as hefty feedback costs
for slow-moving users. Motivated by this, we investigate user-centric LB which
allows users to update their solutions at different paces. The research is
developed upon our previous work on adaptive target-condition neural network
(ATCNN), which can conduct LB for individual users in quasi-static channels. In
this paper, a deep neural network (DNN) model is designed to enable an adaptive
update interval for each individual user. This new model is termed as
mobility-supporting neural network (MSNN). Associating MSNN with ATCNN, a
user-centric LB framework named mobility-supporting ATCNN (MS-ATCNN) is
proposed to handle resource management and mobility management simultaneously.
Results show that at the same level of average update interval, MS-ATCNN can
achieve a network throughput up to 215\% higher than conventional LB methods
such as game theory, especially for a larger number of users. In addition,
MS-ATCNN costs an ultra low runtime at the level of 100s $\mu$s, which is two
to three orders of magnitude lower than game theory.