Empirical study of sensor-aided beam management for 5G-and-beyond systems

RWTH Publications (RWTH Aachen)

Abstract

Millimeter-wave (mm-wave) frequencies are crucial for addressing the growing demand for capacity in fifth generation (5G) mobile networks and beyond.However, they are impacted by unique challenges such as high path loss and susceptibility to blockages.To overcome these challenges, directional communication is implemented with phased antenna arrays.Directional communication requires operations collectively known as beam management, which traditionally incur high overhead and delays.Environmental awareness via sensors has emerged as a candidate to decrease the overhead and delay associated with beam management.In this thesis, two mm-wave testbeds that integrate multiple sensor modalities for the design of novel sensor-aided beam management schemes have been implemented.The first testbed is based on the OpenAirInterface 5G project and enables the development and evaluation of novel sensor-aided beam management protocols in full-stack end-to-end 5G networks.The second testbed serves as a measurement platform to gather multimodal sensor and mm-wave radio frequency data, facilitating the creation of datasets for the development and evaluation of sensor-and machine learning-aided beam management protocols.This thesis provides the details of the implementation of both testbeds, demonstrates their capabilities in example scenarios and measurement campaigns, and discusses the findings from these.The usage of sensors is shown to be highly beneficial for beam management and the analysis of the site-specific propagation characteristics of mm-waves.These testbeds enable the creation of datasets to be used for the development of machine learning/deep learning algorithms for sensor-aided beam management.

Authors 1

1. Berk Acikgöz corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen