AI Model Selection and Monitoring for Beam Management in 5G-Advanced
Chen Sun, Le Zhao, Tao Cui, Haojin Li, Yingshuang Bai, Songtao Wu, Q.K. Tong
Abstract
This paper investigates the application of artificial intelligence (AI) to wireless technology, specifically in the context of beam management (BM) in the advanced 5th-generation (5G) communication system. Our focus lies in aligning our study with the ongoing discussions within the Third Generation Partnership Project (3GPP) as of December 2022. Instead of evaluating the performance of specific AI models, we take user equipment (UE) receiver (Rx) beam prediction as an illustrative example of AI-based BM. We explore various aspects of AI model management, including model selection, monitoring, and activation/deactivation operations, from a 3GPP perspective. For model selection, we propose deploying distinct AI models for different propagation environments, categorized based on base station (BS) transmitter (Tx) beam measurement results. Reference Signal Received Power (RSRP) serves as a pivotal key performance index (KPI) for model performance monitoring. Our simulation results indicate that, instead of training one all-encompassing AI model with numerous layers for universal application, transitioning between domain-specific AI models with fewer layers yields superior performance. Model activation/deactivation procedures determine whether AI-based BM or traditional BM should be employed in a given scenario. We also introduce the use of AI for predicting the performance of both AI-based BM and traditional BM. By comparing the performance of these strategies, we can ascertain whether link performance degradation results from AI output errors or UE movement into challenging propagation environments. This approach enables the effective management of model switching between AI-based BM and traditional BM. The simulation shows that we can reduce the number of unnecessary switches by 10%.