• Corpus ID: 67855738

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

@article{Carrascosa2019DecentralizedAS,
title={Decentralized AP selection using Multi-Armed Bandits: Opportunistic \{\epsilon\}-Greedy with Stickiness},
author={Marc Carrascosa and Boris Bellalta},
journal={arXiv: Networking and Internet Architecture},
year={2019}
}
• Published 1 March 2019
• Computer Science
• arXiv: Networking and Internet Architecture
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…
1 Citations

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## References

SHOWING 1-10 OF 13 REFERENCES
Decentralized AP selection in large-scale wireless LANs considering multi-AP interference
2017 International Conference on Computing, Networking and Communications (ICNC)
• 2017
A decentralized AP selection scheme that takes interference at the candidate APs into account and selects AP that offers best signal-interference-plus noise ratio (SINR) is proposed, which achieves 99% and 43% gains in aggregate throughput over SSF and MPD, respectively.
Channel Selection for Network-Assisted D2D Communication via No-Regret Bandit Learning With Calibrated Forecasting
• Computer Science
IEEE Transactions on Wireless Communications
• 2015
A network-assisted distributed channel selection approach in which D2D users are only allowed to use vacant cellular channels is proposed, and the proposed approach not only yields vanishing regret in comparison to the global optimal solution but also guarantees that the empirical joint frequencies of the game converge to the set of correlated equilibria.
WLC19-4: Effective AP Selection and Load Balancing in IEEE 802.11 Wireless LANs
• Computer Science
IEEE Globecom 2006
• 2006
This paper proposes two effective new AP selection algorithms that can significantly increase overall system throughput and reduce average frame delay and shows that better performance can be achieved when the APs provide assistance in delay measurements.
Cell Breathing Techniques for Load Balancing in Wireless LANs
• Computer Science
IEEE Trans. Mob. Comput.
• 2009
This paper presents a new load balancing technique by controlling the size of WLAN cells (i.e., AP's coverage range), which is conceptually similar to cell breathing in cellular networks, and develops a set of polynomial time algorithms that find the optimal beacon power settings which minimize the load of the most congested AP.
A Neural Network based cognitive engine for IEEE 802.11 WLAN Access Point selection
2012 IEEE Consumer Communications and Networking Conference (CCNC)
• 2012
A Cognitive AP selection scheme that allows the mobile station to learn from its past experience how to select the best AP, and a cognitive engine based on a Neural Network trained on this data drives the AP selection process.
An empirical analysis of the IEEE 802.11 MAC layer handoff process
• Computer Science
CCRV
• 2003
This paper presents an empirical study of this handoff process at the link layer, with a detailed breakup of the latency into various components, showing that a MAC layer function - probe is the primary contributor to the overall handoff latency.
The TMB path loss model for 5 GHz indoor WiFi scenarios: On the empirical relationship between RSSI, MCS, and spatial streams