Improving Spatial Reuse in High-Density Wireless Networks through Learning


Abstract: Popularity of IEEE 802.11 Wireless Local Area Networks (WLANs) leads to massive deployments in which few frequency resources must be shared, resulting in inefficiency in dense environments. The behavior of the protocols that grant access to the medium, which are based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), is inefficient in dense deployments, and prone to generate issues such as the Hidden-Terminal and the Exposed-Terminal problems. Therefore, the overall throughput may be considerably reduced due to collisions and/or starvation. As the complexity of Wireless Networks in terms of variability prevents to computationally find the optimal configuration for a given network, we aim to use Reinforcement Learning (RL) to find close-to-optimal solutions adaptively.

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