Reinforcement Learning; mobile communcations; Reconfigurable intelligent surfaces
en
Abstract:
We consider multi-operator wireless networks where broadband reconfigurable intelligent surfaces (RISs) effectively cover the transmission bands of all operators. These RISs are supplied by a dedicated provider and dynamically leased on-demand to individual operators to support their transmissions. When an operator takes control of a RIS, it can adjust its phase-response to meet the requirements of its users. This sets the stage for a competitive scenario where operators vie for control of RISs. To address this competition, we introduce an auction format designed to efficiently allocate RISs to operators. Furthermore, we develop a multi-agent reinforcement learning environment to optimize operators’ bidding strategies, demonstrating its superiority over the heuristic dominant strategy of greedy bidding.
