Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks

TL;DR

This paper introduces a forecast-based user association method in multi-band cellular networks that improves service rates, reduces handovers, and outperforms traditional and RL-based approaches in efficiency and generalization.

Contribution

It proposes a novel MPC framework combining deep neural network rate forecasting with convex optimization for user association in heterogeneous networks.

Findings

3.5x improvement in 5th percentile service rate

7x reduction in median handovers

100x less training data needed than RL

Abstract

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.