  In recent years, long-term evolution (LTE) and 5G NR (5th Generation New
Radio) technologies have showed great potential to utilize Machine Learning
(ML) algorithms in optimizing their operations, both thanks to the availability
of fine-grained data from the field, as well as the need arising from growing
complexity of networks. The aforementioned complexity sparked mobile operators'
attention as a way to reduce the capital expenditures (CAPEX) and the
operational (OPEX) expenditures of their networks through network management
automation (NMA). NMA falls under the umbrella of Self-Organizing Networks
(SON) in which 3GPP has identified some challenges and opportunities in load
balancing mechanisms for the Radio Access Networks (RANs). In the context of
machine learning and load balancing, several studies have focused on maximizing
the overall network throughput or the resource block utilization (RBU). In this
paper, we propose a novel Clipped Double Q-Learning (CDQL)-based load balancing
approach considering resource block utilization, latency and the Channel
Quality Indicator (CQI). We compare our proposal with a traditional handover
algorithm and a resource block utilization based handover mechanism. Simulation
results reveal that our scheme is able to improve throughput, latency, jitter
and packet loss ratio in comparison to the baseline algorithms.
