# Decentralized AP selection using Multi-Armed Bandits: Opportunistic {\epsilon}-Greedy with Stickiness

## TL;DR

This paper introduces a decentralized multi-armed bandit approach for WiFi AP selection, improving load balancing and resource utilization by enabling STAs to adaptively choose APs based on network conditions.

## Contribution

It proposes a novel Opportunistic ε-greedy with Stickiness algorithm for decentralized AP selection, enhancing convergence speed and network efficiency.

## Key findings

- Reduces network response variability
- Faster convergence to optimal APs
- More efficient network resource utilization

## Abstract

WiFi densification leads to the existence of multiple overlapping coverage areas, which allows user stations (STAs) to choose between different Access Points (APs). The standard WiFi association method makes the STAs select the AP with the strongest signal, which in many cases leads to underutilization of some APs while overcrowding others. To mitigate this situation, Reinforcement Learning techniques such as Multi-Armed Bandits can be used to dynamically learn the optimal mapping between APs and STAs, and so redistribute the STAs among the available APs accordingly. This is an especially challenging problem since the network response observed by a given STA depends on the behavior of the others, and so it is very difficult to predict without a global view of the network. In this paper, we focus on solving this problem in a decentralized way, where STAs independently explore the different APs inside their coverage range, and select the one that better satisfy its needs. To do it, we propose a novel approach called Opportunistic {\epsilon}-greedy with Stickiness that halts the exploration when a suitable AP is found, then, it remains associated to it while the STA is satisfied, only resuming the exploration after several unsatisfactory association periods. With this approach, we reduce significantly the network response variability, improving the ability of the STAs to find a solution faster, as well as achieving a more efficient use of the network resources.