On Scaling Latency-Aware MAC Communication Protocols with a Hierarchical Network Topology
Sarbu, Septimia; Mota, Mateus P.; Bennis, Mehdi (2024-08-20)
IEEE
20.08.2024
S. Sarbu, M. P. Mota and M. Bennis, "On Scaling Latency-Aware MAC Communication Protocols with a Hierarchical Network Topology," ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA, 2024, pp. 2555-2560, doi: 10.1109/ICC51166.2024.10623079.
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202502271869
https://urn.fi/URN:NBN:fi:oulu-202502271869
Tiivistelmä
Abstract
Future 6G wireless networks of connected intelligence will require ultra-high throughput, ultra-low latency, extreme reliability, high levels of scalability and decentralized heterogeneous AI networking. To meet such stringent requirements, high-performing communication protocols become the foundation for the upcoming 6G use-cases. Recently, multi-agent reinforcement learning (MARL) has demonstrated its potential to provide viable alternatives to classical networking for such scenarios. However, while highly competitive, such emerged protocols are not fully explainable and fail to scale to large numbers of users. To bridge this gap, we leverage the advantages of the contention-free MAC protocol, optimize it for two users and integrate it in a hierarchical network architecture, to ensure its high scalability and decreased latency.
Future 6G wireless networks of connected intelligence will require ultra-high throughput, ultra-low latency, extreme reliability, high levels of scalability and decentralized heterogeneous AI networking. To meet such stringent requirements, high-performing communication protocols become the foundation for the upcoming 6G use-cases. Recently, multi-agent reinforcement learning (MARL) has demonstrated its potential to provide viable alternatives to classical networking for such scenarios. However, while highly competitive, such emerged protocols are not fully explainable and fail to scale to large numbers of users. To bridge this gap, we leverage the advantages of the contention-free MAC protocol, optimize it for two users and integrate it in a hierarchical network architecture, to ensure its high scalability and decreased latency.
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