Data-assisted radio resource allocation in shared spectrum multi-RAT heterogeneous network

New Radio Unlicensed (NR-U) is the key representative access technology beyond 5G implementation to alleviate the spectrum crunch. NR-U shares a 5 GHz unlicensed band with WiFi, which has contention challenges for the coexisting systems due to physical and link layer protocols disparity. Being a scheduled access system, NR-U transmissions can only start at strict periodic time slots, which requires introducing a synchronization gap period in the listen-before-talk (LBT) approach. In this paper, we address these issues and analyze the impact of various gap-based NR-U approaches to the fair and efficient coexistence of the two networks. The dependency of successful spectrum access of the two systems on the gap period is also investigated. We also present a machine learning data-driven approach to unlicensed channel selection for spectrum sharing by NR-U. The results based on actual data collected from real-life WiFi deployment scenarios indicate significant improvement in coexistence performance and spectrum utilization of the unlicensed band with the proposed approach. It is shown through simulation results that the gap period before the backoff procedure provides better coexistence performance compared to the gap-based approach, where the synchronization gap is introduced after the LBT backoff. Further, the results indicate that if the gap interval exceeds a certain threshold value for each coexistence scenario, the WiFi network starts dominating the unlicensed channel, completely blocking the NR-U transmissions.