  Millimeter-wave (mmWave) communication is a promising solution to the high
data rate demands in the upcoming 5G and beyond communication networks. When it
comes to supporting seamless connectivity in mobile scenarios, resource and
handover management are two of the main challenges in mmWave networks. In this
paper, we address these two problems jointly and propose a learning-based load
balancing handover in multi-user mobile mmWave networks. Our handover algorithm
selects a backup base station and allocates the resource to maximize the sum
rate of all the users while ensuring a target rate threshold and preventing
excessive handovers. We model the user association as a non-convex optimization
problem. Then, by applying a deep deterministic policy gradient (DDPG) method,
we approximate the solution of the optimization problem. Through simulations,
we show that our proposed algorithm minimizes the number of the events where a
user's rate is less than its minimum rate requirement and minimizes the number
of handovers while increasing the sum rate of all users.
