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The capabilities of the millimeter wave (mmWave) spectrum to fulfill the ultra high data rate demands of V2X (Vehicle-to-Everything) communications necessitates the need for accurate channel modeling to facilitate the efficient development of next-generation network and device design strategies. Ergo, this work describes the design of a novel fully autonomous robotic beam-steering platform, equipped with a custom broadband sliding correlator channel sounder, for 28GHz V2X propagation modeling activities on the NSF POWDER experimental testbed. The compiled datasets constitute geo-positioning logs, alignment specifics, and signal propagation measurements, along unplanned vehicular routes in urban, suburban, and foliage environments. Leveraging a closed-form design exhibiting uninhibited rotational mobility, this beam-alignment platform facilitates the collection of a continuous series of measurements, a distinct yet critical necessity for mmWave channel modeling in vehicular networks. Consequently, the calibrated and post-processed datasets enable crucial propagation analyses necessary for the efficient design and deployment of next-generation V2X networks. Specifically, this paper first studies the pathloss behavior of 28GHz signals along various routes onsite and empirically evaluates the validity of popular outdoor large-scale micro- and macro-cellular pathloss standards—namely, 3GPP TR38.901, ITU-R M.2135, METIS, and mmMAGIC. Next, analyzing the spatial autocorrelation coefficient under distance and antenna alignment accuracy effects delivers unique insights on the decoherence characteristics of 28GHz signals. In addition to shadow fading studies, this paper investigates the fading properties of the obstructed mmWave signal, in terms of its average fade depth and duration, under both static and dynamic blockages. Lastly, using the SAGE algorithm, multipath clustering analyses, centered around the Kolmogorov-Smirnov statistic, facilitate empirical validations of the favored Saleh-Valenzuela, Quasi-Deterministic, and stochastic mmWave channel models vis-à-vis cluster inter-arrival times, cluster decay attributes, and RMS delay and direction spreads.
Keshavamurthy et al. (Thu,) studied this question.