Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G
  Subnetworks
Daniel Abode Ramoni Adeogun Lou Salaün Renato Abreu Thomas Jacobsen Gilberto Berardinelli
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.