On the Analysis of Computational Delays in Reinforcement Learning-based Rate Adaptation Algorithms

2023/03/30 by Ricardo Trancoso, Rúben Queirós, Trancoso, Ricardo +5
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Network Optimization #FOS: Computer and information sciences #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Wireless Networks and Protocols

paper · pdf · doi:10.48550/arxiv.2303.17477

openalex publication_date 2023/03/30 · openalex created_date 2023/04/05 · openalex updated_date 2026/07/28

Abstract

Several research works have applied Reinforcement Learning (RL) algorithms to solve the Rate Adaptation (RA) problem in Wi-Fi networks. The dynamic nature of the radio link requires the algorithms to be responsive to changes in link quality. Delays in the execution of the algorithm may be detrimental to its performance, which in turn may decrease network performance. This aspect has been overlooked in the state of the art. In this paper, we present an analysis of common computational delays in RL-based RA algorithms, and propose a methodology that may be applied to reduce these computational delays and increase the efficiency of this type of algorithms. We apply the proposed methodology to an existing RL-based RA algorithm. The obtained experimental results indicate a reduction of one order of magnitude in the execution time of the algorithm, improving its responsiveness to link quality changes.

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