Channel and Spectrum Consumption Models for Urban Outdoor-to-Outdoor 28 GHz Wireless
Manav Kohli, Carlos E. Caicedo, Tingjun Chen, Irfan Tamim, Angel D. Estigarribia, Tianyi Dai, Igor Kadota, Dmitry Chizhik, Jinfeng Du, Rodolfo Feick, Reinaldo A. Valenzuela, Gil Zussman
Abstract
Millimeter-wave (mmWave) communication has been widely accepted as an enabler of 6G and other next-generation wireless networks, though high path loss strains link budgets, and difficult channel conditions have limited the deployment of mmWave within the 5G NR radio access network (RAN) primarily to dense urban environments. In this paper, we seek to demystify aspects of RAN planning and design for these environments by providing a set of empirical models of the mmWave channel at 28 GHz, alongside a methodology to develop spectrum consumption models (SCMs), which illustrate constraints on spectrum allocation by the RAN. We report on an extensive 28 GHz measurement campaign within the PAWR COSMOS testbed in New York City. This campaign resulted in over 46 million power measurements, collected from over 3,000 links across 24 street sidewalks at four different sites. Using these measurements, we study the effects of the setup and environments, such as TX height and seasonal effects. We then derive a series of channel models for path loss and the azimuth beamforming gain loss, and use them to derive distributions of the link SNR values achievable by UEs on the measured sidewalks. We show, among other results, that 100% of UEs on a given city block can achieve 10 dB SNR at locations with a strong street canyon effect. Finally, we develop a process to generate SCMs based on the IEEE 1900.5.2 standard using the empirical channel models. The generated SCMs facilitate the evaluation of spectrum sharing and interference management scenarios since they capture all directional propagation effects reflected in the measurements and provide a way to easily share the main propagation characterization results derived from the measurements. We believe that the models, methods, and results in this paper will help inform the future of mmWave wireless network deployments within dense urban areas.
