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Beam management (BM) protocols are critical for establishing and maintainingconnectivity between network radio nodes and User Equipments (UEs). InDistributed Multiple Input Multiple Output systems (D-MIMO), a number of accesspoints (APs), coordinated by a central processing unit (CPU), serves a numberof UEs. At mmWave frequencies, the problem of finding the best AP and beam toserve the UEs is challenging due to a large number of beams that need to besounded with Downlink (DL) reference signals. The objective of this paper is toinvestigate whether the best AP/beam can be reliably inferred from soundingonly a small subset of beams and leveraging AI/ML for inference of bestbeam/AP. We use Random Forest (RF), MissForest (MF) and conditional GenerativeAdversarial Networks (c-GAN) for demonstrating the performance benefits ofinference.

Machine Learning (ML)-assisted Beam Management in millimeter (mm)Wave Distributed Multiple Input Multiple Output (D-MIMO) systems