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Millimeter Wave (mmWave) is an important part of 5G new radio (NR), in whichhighly directional beams are adapted to compensate for the substantialpropagation loss based on UE locations. However, the location information mayhave some errors such as GPS errors. In any case, some uncertainty, andlocalization error is unavoidable in most settings. Applying these distortedlocations for clustering will increase the error of beam management. Meanwhile,the traffic demand may change dynamically in the wireless environment.Therefore, a scheme that can handle both the uncertainty of localization anddynamic radio resource allocation is needed. In this paper, we propose aUK-means-based clustering and deep reinforcement learning-based resourceallocation algorithm (UK-DRL) for radio resource allocation and beam managementin 5G mmWave networks. We first apply UK-means as the clustering algorithm tomitigate the localization uncertainty, then deep reinforcement learning (DRL)is adopted to dynamically allocate radio resources. Finally, we compare theUK-DRL with K-means-based clustering and DRL-based resource allocationalgorithm (K-DRL), the simulations show that our proposed UK-DRL-based methodachieves 150% higher throughput and 61.5% lower delay compared with K-DRL whentraffic load is 4Mbps.

Deep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks