Work

ML-Aided Dynamic BSR Periodicity Adjustment for Enhanced UL Scheduling in Cellular Systems

Year: 2025

Type: article

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research has revealed a limitation in the Uplink (UL) Buffer Status Report (BSR) scheduling procedure - its reliance on outdated information.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.

Source: IEEE Open Journal of the Communications Society

DOI: 10.1109/ojcoms.2025.3561002

Authors: Nadezhda Chukhno, Salwa Saafi, Sergey Andreev

Institutions: Tampere University, University of Tampere

Language: English

Topic: Advanced Wireless Network Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075