  In typical wireless cellular systems, the handover mechanism involves
reassigning an ongoing session handled by one cell into another. In order to
support increased capacity requirement and to enable newer use cases, the next
generation wireless systems will have a very dense deployment with advanced
beam-forming capability. In such systems, providing a better mobility along
with enhanced throughput performance requires an improved handover strategy. In
this paper, we will detail a novel method for handover optimization in a 5G
cellular network using reinforcement learning (RL). In contrast to the
conventional method, we propose to control the handovers between base-stations
(BSs) using a centralized RL agent. This agent handles the radio measurement
reports from the UEs and choose appropriate handover actions in accordance with
the RL framework to maximize a long-term utility. We show that the handover
mechanism can be posed as a contextual multi-armed bandit problem and solve it
using Q-learning method. We analyze the performance of the methods using
different propagation and deployment environment and compare the results with
the state-of-the-art algorithms. Results indicate a link-beam performance gain
of about 0.3 to 0.7 dB for practical propagation environments.
