Multi-Band mm-Wave Measurement Platform Towards Environment-Aware Beam Management

TL;DR

This paper presents a multi-band mm-wave measurement platform that integrates multi-modal sensors and machine learning to improve environment-aware beam management for seamless connectivity.

Contribution

It introduces a novel software-defined radio platform with multi-modal sensors, addressing the lack of diverse datasets for environment-aware beam steering in mm-wave systems.

Findings

Demonstrated integration of multi-modal sensors with mm-wave measurement platform

Enabled environment-aware beam management using machine learning techniques

Provided a new dataset for multi-modal mm-wave channel analysis

Abstract

Agile beam management is key for providing seamless millimeter wave (mm-wave) connectivity given the site-specific spatio-temporal variations of the mm-wave channel. Leveraging non radio frequency (RF) sensor inputs for environment awareness, e.g. via machine learning (ML) techniques, can greatly enhance RF-based beam steering. To overcome the lack of diverse publicly available multi-modal mm-wave datasets for the design and evaluation of such novel beam steering approaches, we demonstrate our software-defined radio multi-band mm-wave measurement platform which integrates multi-modal sensors towards environment-aware beam management.