ML-Aided Dynamic BSR Periodicity Adjustment for Enhanced UL Scheduling in Cellular Systems
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Date
2025-04-15
Authors
Chukhno, Nadezhda V.
Saafi, Salwa
Andreev, Sergey
ORCID
0000-0002-2786-9377Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
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Abstract
Contemporary research has revealed a limitation in the Uplink (UL) Buffer Status Report (BSR) scheduling procedure - its reliance on outdated information. In addition, a significant limitation in current BSR implementations lies in their inflexibility. The 3rd Generation Partnership Project (3GPP) specifications constrain BSR periodicities to certain quantized values based on Quality of Service (QoS) requirements for various applications. For instance, applications demanding low latency may require very small BSR periodicities, resulting in substantial overhead due to frequent BSR reports. This may result in the wastage of network resources in case of a low BSR periodicity setting. Alternatively, a high BSR periodicity setting may lead packets to wait more at the user buffer and thus result in higher packet latencies. To address these limitations, we propose a framework that predicts time intervals between packet arrivals and subsequently adjusts the BSR periodicity according to the predicted traffic arrivals. The simulation results demonstrate that the proposed Machine Learning (ML)-aided BSR reporting provides flexibility in BSR periodicity adapted to the intensity of traffic arrival and converges to optimal periodicity depending on the mean traffic arrival rate.
Description
Citation
IEEE Open Journal of the Communications Society. 2025, vol. 6, issue April, p. 3513-3527.
https://ieeexplore.ieee.org/document/10965763
https://ieeexplore.ieee.org/document/10965763
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en