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She, C.Y., Sun, C.J., Gu, Z.Y., Li, Y.H., Yang, C.Y., Poor, H.V. and Vucetic, B. (March 2021) A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning. Proceedings of the IEEE, 109, 204-246. https://doi.org/10.1109/JPROC.2021.3053601

has been cited by the following article:

• TITLE: A Collaborative Machine Learning Scheme for Traffic Allocation and Load Balancing for URLLC Service in 5G and Beyond

  AUTHORS: Andreas G. Papidas, George C. Polyzos

  KEYWORDS: 5G and B5G Networks, Ultra Reliable Low Latency Communications (URLLC), Machine Learning (ML) for 5G, Temporal Difference Methods (TDM), Monte Carlo Methods, Policy Gradient Methods

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.11, November 30, 2023

  ABSTRACT: Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility.