Beam Profiling and Beamforming Modeling for mmWave NextG Networks

TL;DR

This study conducts experimental beam profiling on mmWave testbeds and develops a machine learning model for beamforming, enabling accurate prediction of signal power and data rates to optimize complex 5G network operations.

Contribution

It introduces a novel dataset and machine learning model for beamforming in mmWave networks, enhancing network design and optimization capabilities.

Findings

High prediction accuracy of received signal power and data rate

Effective use of commercial mmWave testbeds at 27 GHz and 71 GHz

Potential applications in network topology, user association, and beam scheduling

Abstract

This paper presents an experimental study on mmWave beam profiling on a mmWave testbed, and develops a machine learning model for beamforming based on the experiment data. The datasets we have obtained from the beam profiling and the machine learning model for beamforming are valuable for a broad set of network design problems, such as network topology optimization, user equipment association, power allocation, and beam scheduling, in complex and dynamic mmWave networks. We have used two commercial-grade mmWave testbeds with operational frequencies on the 27 Ghz and 71 GHz, respectively, for beam profiling. The obtained datasets were used to train the machine learning model to estimate the received downlink signal power, and data rate at the receivers (user equipment with different geographical locations in the range of a transmitter (base station). The results have shown high prediction accuracy with low mean square error (loss), indicating the model's ability to estimate the received signal power or data rate at each individual receiver covered by a beam. The dataset and the machine learning-based beamforming model can assist researchers in optimizing various network design problems for mmWave networks.