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Beam selection for joint transmission in cell-free massive multi-inputmulti-output systems faces the problem of extremely high training overhead andcomputational complexity. The traffic-aware quality of service additionallycomplicates the beam selection problem. To address this issue, we propose atraffic-aware hierarchical beam selection scheme performed in a dual timescale.In the long-timescale, the central processing unit collects wide beam responsesfrom base stations (BSs) to predict the power profile in the narrow beam spacewith a convolutional neural network, based on which the cascaded multiple-BSbeam space is carefully pruned. In the short-timescale, we introduce acentralized reinforcement learning (RL) algorithm to maximize the satisfactionrate of delay w.r.t. beam selection within multiple consecutive time slots.Moreover, we put forward three scalable distributed algorithms includinghierarchical distributed Lyapunov optimization, fully distributed RL, andcentralized training with decentralized execution of RL to achieve betterscalability and better tradeoff between the performance and the executionsignal overhead. Numerical results demonstrate that the proposed schemessignificantly reduce both model training cost and beam training overhead andare easier to meet the user-specific delay requirement, compared to existingmethods.

Traffic-Aware Hierarchical Beam Selection for Cell-Free Massive MIMO