# Transferable and Distributed User Association Policies for 5G and Beyond Networks

arXiv: https://arxiv.org/abs/2106.02540 · HTML: https://mlsanity.com/paper/2106.02540/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames](https://mlsanity.com/paper/1911.00357/) (2019)
- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks](https://mlsanity.com/paper/2006.09066/) (2020)
- [Coverage and Rate Analysis for Millimeter Wave Cellular Networks](https://mlsanity.com/paper/1402.6430/) (2014)
- [User Association in 5G Networks: A Survey and an Outlook](https://mlsanity.com/paper/1509.00338/) (2015)
- [Early Acceptance Matching Game for User Association in 5G Cellular HetNet](https://mlsanity.com/paper/1907.08867/) (2019)

## Built on by (most cited first, 2 of 2)

- [Semantic Channel Equalizer: Modelling Language Mismatch in Multi-User Semantic Communications](https://mlsanity.com/paper/2308.03789/) (2023)
- [Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks](https://mlsanity.com/paper/2307.06842/) (2023)

## Abstract (arXiv)

We study the problem of user association, namely finding the optimal assignment of user equipment to base stations to achieve a targeted network performance. In this paper, we focus on the knowledge transferability of association policies. Indeed, traditional non-trivial user association schemes are often scenario-specific or deployment-specific and require a policy re-design or re-learning when the number or the position of the users change. In contrast, transferability allows to apply a single user association policy, devised for a specific scenario, to other distinct user deployments, without needing a substantial re-learning or re-design phase and considerably reducing its computational and management complexity. To achieve transferability, we first cast user association as a multi-agent reinforcement learning problem. Then, based on a neural attention mechanism that we specifically conceived for this context, we propose a novel distributed policy network architecture, which is transferable among users with zero-shot generalization capability i.e., without requiring additional training.Numerical results show the effectiveness of our solution in terms of overall network communication rate, outperforming centralized benchmarks even when the number of users doubles with respect to the initial training point.