Machine Learning (ML)-assisted Beam Management in millimeter (mm)Wave Distributed Multiple Input Multiple Output (D-MIMO) systems
This addresses beam management inefficiencies in 5G/6G networks for improved connectivity, but it is incremental as it applies existing ML methods to a known bottleneck.
The paper tackles the challenge of finding the best access point and beam for user equipment in millimeter-wave distributed MIMO systems by using AI/ML to infer optimal configurations from a small subset of sounded beams, demonstrating performance benefits with methods like Random Forest, MissForest, and conditional GANs.
Beam management (BM) protocols are critical for establishing and maintaining connectivity between network radio nodes and User Equipments (UEs). In Distributed Multiple Input Multiple Output systems (D-MIMO), a number of access points (APs), coordinated by a central processing unit (CPU), serves a number of UEs. At mmWave frequencies, the problem of finding the best AP and beam to serve the UEs is challenging due to a large number of beams that need to be sounded with Downlink (DL) reference signals. The objective of this paper is to investigate whether the best AP/beam can be reliably inferred from sounding only a small subset of beams and leveraging AI/ML for inference of best beam/AP. We use Random Forest (RF), MissForest (MF) and conditional Generative Adversarial Networks (c-GAN) for demonstrating the performance benefits of inference.