Self-Supervised Learning-Based Scheduling Scheme Under Multiple Access Points Environment for Sub-Terahertz Band

Higher frequency bands are essential for next-generation wireless local area network (LAN). Due to the significant propagation loss of sub-terahertz band, beamforming is indispensable. However, since beamforming is affected by obstructions, stable communication requires simultaneous transmission with multiple access points (APs). In a multi-AP environment, although optimal resource scheduling can be determined through exhaustive search, the complexity is quite high. In this letter, we evaluate resource scheduling using self-supervised learning, which does not rely on teacher labels for training. By computer simulation, we show that self-supervised learning can achieve comparable to that of exhaustive search.