2023/03/28 by Tiago Koketsu Rodrigues, Rodrigues, Tiago Koketsu, Shikhar Verma +9 · 1 citation
Engineering · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #IPv6, Mobility, Handover, Networks, Security #Networking and Internet Architecture (cs.NI) #Telecommunications and Broadcasting Technologies
paper · pdf · doi:10.48550/arxiv.2303.15731
openalex publication_date 2023/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. They can offer extremely high transmission rates but at the cost of low range and penetration. Due to these issues, WiGig systems are unstable and need to rely on frequent handovers to maintain high-quality connections. However, this solution is problematic as it forces users into bad connections and downtime before they are switched to a better access point. In this work, we use Machine Learning to identify patterns in user behaviors and predict user actions. This prediction is used to do proactive handovers, switching users to access points with better future transmission rates and a more stable environment based on the future state of the user. Results show that not only the proposal is effective at predicting channel data, but the use of such predictions improves system performance and avoids unnecessary handovers.