FTP: Enabling Fast Beam-Training for Optimal mmWave Beamforming

摘要

To maximize Signal-to-Noise Ratio (SNR), it is necessary to move beyond selecting beams from a codebook. While the state-of-the-art approaches can significantly improve SNR compared to codebook-based beam selection by exploiting the globally-optimal beam, they incur significant beam-training overhead, which limits the applicability to large-scale antenna arrays and the scalability for multiple users. In this paper, we propose FTP, a highly-scalable beam-training solution that can find the globally-optimal beam with minimal beam-training overhead. FTP works by estimating per-path direction along with its complex gain and synthesizes the globally-optimal beam from these parameters. Our design significantly reduces the search space for finding such path parameters, which enables FTP to scale to large-scale antenna arrays. We implemented and evaluated FTP on a mmWave experimental platform with 32 antenna elements. Our results demonstrate that FTP achieves optimal SNR performance comparable with the state-of-the-art while reducing the beam-training overhead by 3 orders of magnitude. Under simulated settings, we demonstrate that the gain of FTP can be even more significant for larger antenna arrays with up to 1024 elements.