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Achieving Proportional Fairness in WiFi Networks via Bandit Convex Optimization

Abstract : We revisit in this paper proportional fair channel allocation in IEEE 802.11 networks. Instead of following traditional approaches based on explicit solution of the optimization problem or iterative solvers, we investigate the use of a bandit convex optimization algorithm. We propose an algorithm which is able to learn the optimal slot transmission probability only by monitoring the throughput of the network. We have evaluated this algorithm both using the true value of the function to optimize, as well as adding estimation errors coming from a network simulator. By means of the proposed algorithm, we provide extensive experimental results which illustrate the sensitivity of the algorithm to different learning parameters and noisy estimates. We believe this is a practical solution to improve the performance of wireless networks that does not require inferring network parameters.
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https://hal.inria.fr/hal-03266460
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Submitted on : Monday, June 21, 2021 - 5:31:42 PM
Last modification on : Friday, July 30, 2021 - 2:41:22 PM
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Golshan Famitafreshi, Cristina Cano. Achieving Proportional Fairness in WiFi Networks via Bandit Convex Optimization. 2nd International Conference on Machine Learning for Networking (MLN), Dec 2019, Paris, France. pp.85-98, ⟨10.1007/978-3-030-45778-5_7⟩. ⟨hal-03266460⟩

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