  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.
