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

arXiv: https://arxiv.org/abs/2301.09294 · HTML: https://mlsanity.com/paper/2301.09294/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2023.

## Built on by (most cited first, 4 of 4)

- [Large Vision Model-Enhanced Digital Twin with Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks](https://mlsanity.com/paper/2410.07611/) (2024)
- [Map2Traj: Street Map Piloted Zero-shot Trajectory Generation with Diffusion Model](https://mlsanity.com/paper/2407.19765/) (2024)
- [CHOMET: Conditional Handovers via Meta-Learning](https://mlsanity.com/paper/2507.07581/) (2025)
- [Meta-Learning-Based Handover Management in NextG O-RAN](https://mlsanity.com/paper/2512.22022/) (2025)

## Abstract (arXiv)

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.