Move Away From Me! User Repulsion Under Proximity-Induced Interference in OWC Systems

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.