Agile beam management is key to seamless high-speed mm-wave connectivity in the beyond-5G era, given the site-specific spatio-temporal variations of the mm-wave channel. Leveraging non-RF sensor inputs for environment awareness, e.g. via ML techniques, can greatly enhance RF-based beam management. To address the lack of diverse publicly available multi-modal mm-wave datasets for the design of novel beam management approaches and to enable their real-world, real-time evaluation, we present our SDR-based multi-band mm-wave experimental platform which integrates multi-modal sensors towards environment-aware beam management.

Wireless communication plays an important role in the digitization of industries. A 5G cellular communication system enables several industrial automation use cases. Fifth-generation deployments in industrial use cases have mainly been carried out in the sub-7 GHz frequency range. In this work, we empirically study 5G system performance in the millimeter wavelength (mmW) range for industrial use cases: additive manufacturing processes and precision manufacturing robotics. We carry out an experimental performance evaluation of a commercially available non-public 5G mmW system to assess its latency, reliability and throughput for uplink and downlink data traffic in a real industrial environment. We also investigate the impact of various 5G configurations on 5G performance characteristics with insights from the baseband log information as well as unidirectional latency measurements. Our empirical results indicate that 5G mmW system can achieve low latency with high reliability in both one-way traffic directions. The throughput is observed to be high for line-of-sight (LOS) scenarios, making the use of the 5G mmW system appealing especially for data rate-intensive and time-critical industrial use cases. We also observe that industrial environments with lots of metal and reflective surfaces provide favorable propagation conditions for non-LOS transmissions. Our results indicate that static industrial use cases with low mobility can leverage the performance benefits of 5G mmW systems.

Reconfigurable intelligent surfaces (RISs) offer programmable control of radio propagation for future wireless systems. For configuration, geometry-driven analytical approaches are appealing for their simplicity and real-time operation, but their performance in challenging environments such as industrial halls with dense multipath and metallic scattering is not well established. To this end, we present a measurement-based evaluation of geometry-driven RIS beam steering in a large industrial hall using a 5 GHz RIS prototype. A novel RIS configuration is proposed in which four patch antennas are mounted in close proximity in front of the RIS to steer the incident field and enable controlled reflection. For this setup, analytically computed, quantized configurations are implemented. Two-dimensional received power maps from two measurement areas reveal consistent, spatially selective focusing. Configurations optimized near the receiver produce clear power maxima, while steering to offset locations triggers a rapid 20-30 dB reduction. With increasing RIS-receiver distance, elevation selectivity broadens due to finite-aperture and geometric constraints, while azimuth steering remains robust. These results confirm the practical viability of geometry-driven RIS beam steering in industrial environments and support its use for spatial field control and localization under non-ideal propagation.