Reinforcement Learning-Aided Distributed User-to-Access Points Association in Interfering Networks

Abstract

In future wireless networks, more and more users will be requiring various applications provided by multiple wireless interfaces simultaneously. This poses significant challenges for enabling efficient wireless resource sharing while satisfying the diverse and stringent Quality of Service (QoS) constraints, especially in dense interfering networks. In such a context, this work proposes distributed user-to-multiple Access Points (AP) association methods, where a user requiring several applications may be served by several APs simultaneously. The problem is formulated as a network sum-rate maximization subject to the required QoS constraints for each user and application, and AP load constraints. In the proposed distributed association methods, each user can decide to associate to multiple APs simultaneously using its locally available network information, leveraging reinforcement learning techniques. Simulation results show that, compared to a baseline scheme, the proposed methods enable large throughput enhancements while satisfying the QoS constraints and AP load limitations, thereby reducing user outage probabilities.