  Cellular networks are becoming increasingly heterogeneous with higher base
station (BS) densities and ever more frequency bands, making BS selection and
band assignment key decisions in terms of rate and coverage. In this paper, we
decompose the mobility-aware user association task into (i) forecasting of user
rate and then (ii) convex utility maximization for user association accounting
for the effects of BS load and handover overheads. Using a linear combination
of normalized mean-squared error and normalized discounted cumulative gain as a
novel loss function, a recurrent deep neural network is trained to reliably
forecast the mobile users' future rates. Based on the forecast, the controller
optimizes the association decisions to maximize the service rate-based network
utility using our computationally efficient (speed up of 100x versus generic
convex solver) algorithm based on the Frank-Wolfe method. Using an
industry-grade network simulator developed by Meta, we show that the proposed
model predictive control (MPC) approach improves the 5th percentile service
rate by 3.5x compared to the traditional signal strength-based association,
reduces the median number of handovers by 7x compared to a handover agnostic
strategy, and achieves service rates close to a genie-aided scheme.
Furthermore, our model-based approach is significantly more sample-efficient
(needs 100x less training data) compared to model-free reinforcement learning
(RL), and generalizes well across different user drop scenarios.
