Learning-Based Model Predictive Control for User Association in Multi-Band Mobile Networks

As cellular networks embrace heterogeneity with higher base station (BS) densities and ever more frequency bands, BS selection and band assignment become increasingly key decisions in terms of rate and coverage optimization. In this paper, we propose a novel learning-based model predictive control (MPC) approach for BS selection and band assignment while accounting for user mobility. We formulate a convex utility maximization problem that accounts for the effects of BS load and handover overheads on the user's service rate. Using a linear combination of normalized mean-squared error (NMSE) and
$\mathbf{top}-m$
loss as a novel loss function, a recurrent deep neural network is trained to reliably forecast the mobile users' future rates. The MPC controller then uses this forecast to optimize the association decisions to maximize the service rate-based network utility. Using an industry-grade network simulator developed by Meta, we show that the proposed approach improves the 5th percentile service rate by
$2.7\times$
compared to the traditional signal strength-based association and its performance approaches that of a genie-aided scheme in terms of the achieved service rate and the number of handovers triggered.