Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks
This addresses interference coordination in factory wireless networks, but it is incremental as it builds on existing graph coloring and Potts model heuristics.
The paper tackles the problem of frequency sub-band allocation in dense 6G subnetworks to manage interference, achieving performance close to a centralized greedy heuristic with lower computational time and reduced signaling overhead.
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.