Learning-Based Neighboring Station Coverage Identification and Dynamic Resource Utilization for 3-D Cells in NR-U Networks

With ongoing proliferation of wireless networks and dramatic growth of data traffic, offloading cellular network traffic to unlicensed band (UB) enabled new radio (NR) base stations (BSs) becomes one of the critical alternatives to address the radio resource constraint. Due to network densification and reduced small cell coverage, this solution can be further improved through efficient and dynamic utilization of 3-D spatially distributed common UB radio resources. The overall objective of this study is to better estimate the 3-D location-specific interference from neighboring UB BSs and to support efficient and dynamic radio resource utilization in the overlapped coverage space. In the first part of this study, the problem of determining the location-specific interference of a neighbor NR-Unlicensed (NR-U) BS is addressed. This problem is solved with a deep regression neural network assisted algorithm while predicting 3-D location-based interference power of a neighbor NR-U BS and bypassing the complex path loss parameter estimation processes. In the second part of this study, the problem of efficient and dynamic UB radio resource utilization is considered for NR-U transmissions. To solve this problem, a collaborative double
$Q$
-learning (DQL) algorithm is developed. Under ideal, estimated and theoretical categories of predictions, approximately 11%, 23%, and 20% better performance and approximately 59%, 212%, and 67% faster algorithm convergence are shown by the DQL assisted approach over conventional
$Q$
-learning-based solutions, respectively.