Wireless local area networks (WLANs) manage multiple access points (APs) and assign scarce radio frequency resources to APs for satisfying traffic demands of associated user devices. This paper considers the channel allocation problem in WLANs that minimizes the mutual interference among APs, and puts forth a learning-based solution that can be implemented in a decentralized manner. We formulate the channel allocation problem as an unsupervised learning problem, parameterize the control policy of radio channels with graph neural networks (GNNs), and train GNNs with the policy gradient method in a model-free manner. The proposed approach allows for a decentralized implementation due to the distributed nature of GNNs and is equivariant to network permutations. The former provides an efficient and scalable solution for large network scenarios, and the latter renders our algorithm independent of the AP reordering. Empirical results are presented to evaluate the proposed approach and corroborate theoretical findings.
翻译:无线局域网管理多个接入点,并将稀少的无线电频率资源分配给亚太局,以满足相关用户装置的交通需求。本文件审议了各局域网的频道分配问题,最大限度地减少亚太局之间的相互干扰,并提出可以分散实施的基于学习的解决办法。我们将频道分配问题作为一个不受监督的学习问题提出来,将无线电频道的控制政策与平面神经网络(GNNs)参数化,并以无模式方式为全球网网点提供政策梯度方法的培训。拟议办法允许由于全球网点分布的性质而分散实施,并且对网络变异具有等性。前者为大型网络情景提供了高效且可扩展的解决方案,而后者使我们的算法与亚太网的重新排序脱钩。我们提出了经验性的结果,以评价拟议的方法并证实理论结论。