# Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks

arXiv: https://arxiv.org/abs/2006.09066 · HTML: https://mlsanity.com/paper/2006.09066/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges](https://mlsanity.com/paper/2405.17454/) (2024)
- [Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences](https://mlsanity.com/paper/2305.00135/) (2023)
- [Large Language Model-Driven Curriculum Design for Mobile Networks](https://mlsanity.com/paper/2405.18039/) (2024)
- [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)
- [Transferable and Distributed User Association Policies for 5G and Beyond Networks](https://mlsanity.com/paper/2106.02540/) (2021)
- [Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning](https://mlsanity.com/paper/2103.16985/) (2021)
- [Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach](https://mlsanity.com/paper/2608.14599/) (2026)
- [Multi-Agent Q-Learning for Real-Time Load Balancing User Association and Handover in Mobile Networks](https://mlsanity.com/paper/2412.19835/) (2024)
- [Deep Reinforcement Learning-Based User Association in Hybrid LiFi/WiFi Indoor Networks](https://mlsanity.com/paper/2503.01803/) (2025)
- [Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints](https://mlsanity.com/paper/2608.01745/) (2026)

## Abstract (arXiv)

Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks. In this context, designing low-complexity policies with local observations, yet able to adapt the user association with respect to the global network state and to the network dynamics is a challenge. In fact, the frameworks proposed in literature require continuous access to global network information and to recompute the association when the radio environment changes. With the complexity associated to such an approach, these solutions are not well suited to dense 5G networks. In this paper, we address this issue by designing a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. In this approach, users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Since there is no direct information exchange among the agents, we also limit the signaling overhead. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.