As communication systems shift towards ever higher
frequency bands, the propagation of signal between a user
device and an infrastructure becomes more susceptible to nearby
obstacles including other users. As an extreme case, we consider
such proximity-induced channel impairments in indoor optical
wireless communication (OWC) systems. We set up a model,
where the achievable OWC data rate depends not only on the
relative position between a user device and an infrastructure
access point, but also on the location of other users modeled
as proximal interferers. We use a reinforcement learning (RL)
approach to enable users to find suitable positions, both relative
to the access point and to each other, that maximise the sum-
rate capacity of the system. Our initial results demonstrate a
feasibility of RL-based approach that enables indoor OWC users
to find suitable balance between establishing high-rate direct link
while remaining distant from proximal interferers
