  To optimally cover users in millimeter-Wave (mmWave) networks, clustering is
needed to identify the number and direction of beams. The mobility of users
motivates the need for an online clustering scheme to maintain up-to-date beams
towards those clusters. Furthermore, mobility of users leads to varying
patterns of clusters (i.e., users move from the coverage of one beam to
another), causing dynamic traffic load per beam. As such, efficient radio
resource allocation and beam management is needed to address the dynamicity
that arises from mobility of users and their traffic. In this paper, we
consider the coexistence of Ultra-Reliable Low-Latency Communication (URLLC)
and enhanced Mobile BroadBand (eMBB) users in 5G mmWave networks and propose a
Quality-of-Service (QoS) aware clustering and resource allocation scheme.
Specifically, Density-Based Spatial Clustering of Applications with Noise
(DBSCAN) is used for online clustering of users and the selection of the number
of beams. In addition, Long Short Term Memory (LSTM)-based Deep Reinforcement
Learning (DRL) scheme is used for resource block allocation. The performance of
the proposed scheme is compared to a baseline that uses K-means and
priority-based proportional fairness for clustering and resource allocation,
respectively. Our simulation results show that the proposed scheme outperforms
the baseline algorithm in terms of latency, reliability, and rate of URLLC
users as well as rate of eMBB users.
