Empirical Analysis of Statistical Variation in Channel Data of WiGig Networks Towards 6G

Emerging wireless local area networks, such as WiGig that operate in the extremely high-frequency band (60 GHz) hold significant potential for the development of next-generation 6G networks by offering high throughput and low latency. However, the 60 GHz band is prone to severe signal degradation due to channel blockages, leading to frequent handovers and challenges in maintaining seamless connectivity. Reactive handover strategies can result in service delays due to overhead and decision-making latency. To tackle these issues, proactive approaches that utilize machine learning (ML) and deep learning (DL) are becoming increasingly popular for network optimization in WiGig networks. However, existing ML/DL models are often tailored to specific network environments, making them susceptible to concept drift — a phenomenon where even minor environmental changes can significantly degrade network performance due to incorrect decision-making. This paper investigates scenarios and environmental changes that can trigger concept drift in WiGig networks. We conduct real-world experiments to analyze the statistical behavior of received signal strength, highlighting the potential for concept drift. Based on our findings, we propose a direction for identifying concept drift in WiGig networks.