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

• URL: http://arxiv.org/abs/2401.05422v1
• Date: Sat, 30 Dec 2023 09:24:19 GMT
• Title: Machine Learning (ML)-assisted Beam Management in millimeter (mm)Wave Distributed Multiple Input Multiple Output (D-MIMO) systems
• Authors: Karthik R M, Dhiraj Nagaraja Hegde, Muris Sarajlic, Abhishek Sarkar
• Abstract summary: This paper investigates whether the best AP/beam can be reliably inferred from sounding only a small subset of beams. We use Random Forest (RF), MissForest (MF) and conditional Generative Adversarial Networks (c-GAN) for demonstrating the performance benefits of inference.
• Score: 0.5079602839359522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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