Fast Best Beam Prediction and Overhead Reduction for 6G Networks: A Deep Learning Approach

作者：Jalal Jalali, Juan Roa, Yifei Song, Renjian Zhao, Baoling Sheen · 年份：2024 · DOI：10.1109/vtc2024-spring62846.2024.10683207 · 被引用次数：10 · 研究领域：Advanced Photonic Communication Systems、Photonic and Optical Devices、Millimeter-Wave Propagation and Modeling

Beam management (BM) plays a crucial role in maintaining reliable communication links in highly dynamic scenarios. To enhance BM performance, the 3rd Generation Partnership Project (3GPP) is actively exploring the use of artificial intelligence (AI) and machine learning (ML) for beam prediction in the evolution toward sixth-generation (6G) communications. The main goals of these 3GPP-based standard studies are to minimize the overhead from reference signals (RSs) and to reduce the number of beam sweepings at the user equipment (UE), which arise due to frequent beam measurements caused by UE movement and rotation. This paper delves into an AI/ML algorithm design that supports spatial domain beam prediction tailored for BM in 6G. This includes forecasting the optimal beam (pairs) and anticipating beam changes. Simulations are based on a data-driven strategy that uses RS receive power (RSRP) measurements as input for fast beam pair prediction with an advanced convolutions neural network (CNN) architecture. Results indicate that the proposed AI/ML model outperforms conventional BM techniques, reducing beam sweeping overhead and thereby validating the AI/ML's potential in BM. Additionally, our proposed algorithm achieves up to 40.58% higher beam prediction accuracy and improves the mean RSRP difference of the predicted best beam pair by up to 2.89 d$B$.