Reinforcement Based User Scheduling for Cellular Communications

Nimrod Gradus, Asaf Cohen, Erez Biton, Omer Gurwitz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Scheduling in cellular networks is one of the most influential factors in performance in wireless deployments such as 4G and 5G and is one of the most challenging and influential resource allocation tasks performed by the base station. It requires the handling of two important performance metrics, throughput and fairness. Fundamentally, these two metrics challenge one another, and maximization of one might come at the expense of the other. On the one hand maximizing the throughput, which is the goal of many communication networks, requires allocating the resources to users with better channel conditions. On the other hand, fairness requires allocating some resources to users with poor channel conditions. One of the prevalent scheduling schemes relies on maximization of the proportional fairness criterion that balances between the two aforementioned metrics with minimal compromise. Proportional fairness based schedulers commonly rely on a greedy approach in which each resource block is allocated to the user that maximizes the proportional fairness criterion. However, typically users can tolerate some delay especially if it boosts their performance. Motivated by this assertion, we suggest a reinforcement-based proportional-fair scheduler for cellular networks. The suggested scheduler incorporates users’ channel estimates together with predicted future channel estimates in the process of resource allocation, in order to maximize the proportional fairness criterion in predefined periodic time epochs. We developed a reinforcement learning tool that learns the users’ channel fluctuations and decides upon the best user selection at each time slot in order to achieve the best fairness in throughput trade-off over multiple time slots. We demonstrate through simulations how such a scheduler outperforms the standardized proportional fairness. We further implemented the suggested scheme on a real live 4G base station, also known as an EnodeB, and showed similar gains.

Original languageEnglish
Title of host publicationCyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
EditorsShlomi Dolev, Amnon Meisels, Jonathan Katz
PublisherSpringer Cham
Pages189-207
Number of pages19
Volume13301
ISBN (Electronic)978-3-031-07689-3
ISBN (Print)9783031076886
DOIs
StatePublished - 2022
Event6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 - Beer Sheva, Israel
Duration: 30 Jun 20221 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13301 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
Country/TerritoryIsrael
CityBeer Sheva
Period30/06/221/07/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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