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.
Beam management (BM) protocols play a crucial role in ensuring the establishment and maintenance of connectivity between network radio nodes and User Equipments (UEs). In Distributed Multiple Input Multiple Output systems (D-MIMO), a group of access points (APs) coordinated by a central processing unit (CPU) serve multiple UEs. However, at mmWave frequencies, the task of determining the optimal AP and beam to serve the UEs becomes extremely challenging due to the large number of beams that need to be explored using Downlink (DL) reference signals.
This paper aims to address this challenge by exploring the possibility of reliably inferring the best AP/beam by sampling only a small subset of beams and leveraging the power of artificial intelligence/machine learning (AI/ML) techniques for inference. The authors use three different AI/ML algorithms, namely Random Forest (RF), MissForest (MF), and conditional Generative Adversarial Networks (c-GAN), to demonstrate the performance benefits of such an inference approach.
The use of RF, MF, and c-GAN algorithms offers a multi-disciplinary perspective to the problem of beam management in D-MIMO systems. Random Forest is a powerful machine learning algorithm popularly used for classification and regression tasks. It can effectively handle complex and noisy data, making it suitable for inferring the best AP/beam from a limited set of sampled beams. MissForest, on the other hand, is specifically designed to handle missing data, which is highly relevant in scenarios where only a subset of beams are sounded. Finally, the conditional Generative Adversarial Networks (c-GAN) algorithm provides a unique approach to generating synthetic data that bridges the gap between the limited subset of sounded beams and the complete beam dataset.
By applying these AI/ML techniques to beam management in D-MIMO systems, the authors demonstrate the potential benefits of using inference methods to determine the best AP and beam with high accuracy. Not only can this significantly reduce the overhead of sounding all beams, but it can also improve the overall performance and efficiency of the system by enabling faster and more reliable beam selection. The combination of AI/ML and beam management in this study highlights the importance of interdisciplinary research in addressing complex challenges in telecommunications.
Looking ahead, further research could explore the use of other AI/ML algorithms and techniques to enhance the inference process even more. Additionally, investigating the impact of different factors such as network conditions, UE mobility, and beamforming capabilities on the performance of the AI/ML-based inference methods would provide valuable insights for optimizing beam management protocols. Overall, this study opens up new avenues for innovation in D-MIMO systems and showcases the potential of AI/ML in revolutionizing beam management in wireless networks.