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We study the problem of user association, namely finding the optimalassignment of user equipment to base stations to achieve a targeted networkperformance. In this paper, we focus on the knowledge transferability ofassociation policies. Indeed, traditional non-trivial user association schemesare often scenario-specific or deployment-specific and require a policyre-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, withoutneeding a substantial re-learning or re-design phase and considerably reducingits computational and management complexity. To achieve transferability, wefirst cast user association as a multi-agent reinforcement learning problem.Then, based on a neural attention mechanism that we specifically conceived forthis context, we propose a novel distributed policy network architecture, whichis transferable among users with zero-shot generalization capability i.e.,without requiring additional training.Numerical results show the effectivenessof our solution in terms of overall network communication rate, outperformingcentralized benchmarks even when the number of users doubles with respect tothe initial training point.

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