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Fifth-generation (5G) new radio (NR) deployments are being rolled out in both the C–band (3.3 - 5.0 GHz) and millimeter-wave (mmWave) band (24.5 - 29.5 GHz). For outdoor scenarios, the C–band is expected to provide wide area coverage and throughput uniformity, whereas the mmWave band is expected to provide ultra-high throughput to dedicated areas within the C-band coverage. Due to the differences in the frequency bands, both systems are expected to be designed with different transmit and receive parameters, naturally resulting in performance variations proportional to the chosen parameters. Unlike many previous works, this paper presents measurement evaluations in central Auckland, New Zealand, from a precommercial deployment of a single-user, single-cell 5G-NR system operating in both bands. The net throughput, coverage reliability, and channel rank are analyzed across the two bands with baseband and analog beamforming. Our results show that the C-band coverage is considerably better than mmWave, with a consistently higher channel rank. Furthermore, the spatial stationarity region (SSR) for the azimuth angles-of-departure (AODs) is characterized, and a model derived from the measured beam identities is presented. The SSR of azimuth AODs is seen to closely follow a gamma distribution. 

Development of a comprehensive channel propagation model for high-fidelity design and deployment of wireless communication networks necessitates an exhaustive measurement campaign in a variety of operating environments and with different configuration settings. As the campaign is time-consuming and expensive, the effort is typically shared by multiple organizations, inevitably with their own channelsounder architectures and processing methods. Without proper benchmarking, it cannot be discerned whether observed differences in the measurements are actually due to the varying environments or to discrepancies between the channel sounders themselves. The simplest approach for benchmarking is to transport participant channel sounders to a common environment, collect data, and compare results. Because this is rarely feasible, this paper proposes an alternative methodology – which is both practical and reliable – based on a mathematical system model to represent the channel sounder. The model parameters correspond to the hardware features specific to each system, characterized through precision, in situ calibration to ensure accurate representation; to ensure fair comparison, the model is applied to a ground-truth channel response that is identical for all systems. Five worldwide organizations participated in the cross-validation of their systems through the proposed methodology. Channel sounder descriptions, calibration procedures, and processing methods are provided for each organization as well as results and comparisons for 20 ground-truth channel responses. 

The paper deals with an analysis of multipath propagation environment in the 60 GHz band using a pseudo-random binary sequence-based time-domain channel sounder with 8 GHz bandwidth. The main goal of this work is to analyze the multipath components (MPCs) propagation between a moving car carrying a transmitter with an omnidirectional antenna and a fixed receiver situated in a building equipped with a manually steered directional horn antenna. The paper briefly presents the time dependence of the dominant MPC magnitudes, shows the effect of the surrounding vegetation on the RMS delay spread and signal attenuation, and statistically evaluates the reflective properties of the road which creates the dominant reflected component. To understand how the MPCs propagate through the channel we measured and analyzed the power and the RMS delay spread distributions in the static environment surrounding the car using an automated measuring system with a controlled receiver antenna tracking system. We give some examples of how the MPC magnitudes change during the antenna tracking and demonstrate that a building and a few cars parked close to the measuring car create a lot of MPCs detectable by the setup with a dynamic range of about 50 dB. 

Utilizing millimeter-wave (mmWave) frequencies for wireless communication in mobile systems is challenging since it requires continuous tracking of the beam direction. Recently, beam tracking techniques based on channel sparsity and/or Kalman filter-based techniques were proposed where the solutions use assumptions regarding the environment and device mobility that may not hold in practical scenarios. In this paper, we explore a machine learning-based approach to track the angle of arrival (AoA) for specific paths in realistic scenarios. In particular, we use a recurrent neural network (R-NN) structure with a modified cost function to track the AoA. We propose methods to train the network in sequential data, and study the performance of our proposed solution in comparison to an extended Kalman filter based solution in a realistic mmWave scenario based on stochastic channel model from the QuaDRiGa framework. Results show that our proposed solution outperforms an extended Kalman filter-based method by reducing the AoA outage probability, and thus reducing the need for frequent beam search. 

This paper presents results obtained from a vehicle- to-vehicle channel measurement campaign carried out in the millimeter-wave band around a 60 GHz center frequency and with 8 GHz of bandwidth. We characterize a situation of two oncoming cars on a two-lane road in the campus of the Brno University of Technology. For several vehicle passes we evaluate: (1) observed root mean square (RMS) delay spreads as a function of the received power, (2) temporal decorrelation of the channel impulse response and (3) a dependency of the Pearson correlation coefficient on the received power. For the measurement campaign, a correlative time-domain channel sounder was used.