Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks

Autor:	Abode, Daniel, Adeogun, Ramoni, Salaün, Lou, Abreu, Renato, Jacobsen, Thomas, Berardinelli, Gilberto
Rok vydání:	2023
Předmět:	Computer Science - Networking and Internet Architecture Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	In this paper, we present an unsupervised approach for frequency sub-band allocation in wireless networks using graph-based learning. We consider a dense deployment of subnetworks in the factory environment with a limited number of sub-bands which must be optimally allocated to coordinate inter-subnetwork interference. We model the subnetwork deployment as a conflict graph and propose an unsupervised learning approach inspired by the graph colouring heuristic and the Potts model to optimize the sub-band allocation using graph neural networks. The numerical evaluation shows that the proposed method achieves close performance to the centralized greedy colouring sub-band allocation heuristic with lower computational time complexity. In addition, it incurs reduced signalling overhead compared to iterative optimization heuristics that require all the mutual interfering channel information. We further demonstrate that the method is robust to different network settings. Comment: Accepted in VTC Fall 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.00950 Zobrazit plný text záznamu View this record from Arxiv