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

TL;DR

This paper introduces a novel distributed multi-agent reinforcement learning approach with neural attention for user association in 5G networks, enabling transferability across different scenarios without retraining.

Contribution

It proposes a transferable, zero-shot generalizable distributed policy network for user association, reducing re-learning needs in dynamic 5G environments.

Findings

Outperforms centralized benchmarks in communication rate

Maintains effectiveness when user numbers double

Enables scenario transferability without retraining

Abstract

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.