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.

该论文通过基于图的学习提出了一种无监督的无线网络频率子频带分配方法，模型化了子网络部署为冲突图，并借鉴了图着色启发式和波茨模型的无监督学习方法来优化子频带的分配，数值评估表明该方法在计算时间复杂度更低的前提下，实现了接近集中式贪婪着色子频带分配启发式的性能，并且与需要所有互相干扰信道信息的迭代优化启发式相比，它产生了较少的信令开销，进一步证明了该方法对不同网络环境的鲁棒性。