Machine Learning Based Time Domain Millimeter-Wave Beam Prediction for 5G-Advanced and Beyond: Design, Analysis, and Over-The-Air Experiments

Abstract

Artificial intelligence (AI) or machine learning (ML) based beam prediction is currently studied in the 3rd Generation Partnership Project (3GPP) fifth generation (5G)-Advanced new ratio (NR) standardization for future commercialization and standard evolution towards sixth generation (6G) communications, wherein time domain (TD) beam prediction is an important use case. The targets for such 3GPP studies and standardization are to lower power consumed at user equipment (UE) and reference signal (RS) overhead that are currently needed by frequent beam measurements due to UE rotation and mobility. To meet such targets, in this paper, we investigate AI/ML based algorithms facilitating TD beam prediction suitable for 5G-Advanced beam management (BM), including RS receive power (RSRP) prediction and beam change prediction. The proposed AI/ML algorithms are first evaluated through computer simulations with new UE mobility models based on recent standard evolutions in 3GPP. Then we further present over-the-air test results achieved by such AI/ML algorithms, using based station (BS) and UE compliant with 3GPP standards. Evaluation results show that the proposed schemes can accurately predict future beams and reduce large amount of the power consumed at the UE for BM, which also demonstrate feasibility of AI/ML based BM for 5G-Advanced and future 6G communications.