Fifth generation new radio vehicle-to-everything (5G-V2X) communication is an emerging technology to support advanced use cases and higher automation levels in Internet of Vehicles. A comprehensive and accurate knowledge of narrow-beam channels plays a crucial role in the utilization of the 5G-V2X technique. This paper focuses on measurement and characterization of vehicle-to-infrastructure (V2I) narrow-beam channel. A flexible narrow-beam channel measurement system using a phased-array antenna is designed, and is employed to perform a series of 3.35 GHz channel measurements with different beam widths in highway primary road and auxiliary road. Based on the collected data, the fading characteristics of V2I narrow-beam channel including path loss, shadow fading and K-factor are extracted, analyzed and then modeled. Then, the V2I narrow-beam channel dispersion in time-frequency-space domain is characterized, and the statistical models of root-mean-square (RMS) delay spread, RMS Doppler spread and RMS angular spread are proposed. In addition, the V2I narrow-beam channel nonstationarity is discussed in terms of the stationarity interval and birth-death process of multipath components, and results of the Markov chain model with parameters like state transition probability matrix and steady-state probability are reported. The results can contribute to the design and evaluation of 5G-V2X technology.

Vehicle-to-infrastructure (V2I) communications have attracted much attention in recent years due to its application in intelligent transportation systems. To apply the dynamic beamforming technology in V2I communications, an accurate V2I narrow-beam channel model is required. This paper investigates cluster-based dynamic narrow-beam channel modeling for V2I scenarios. Firstly, a phased-array antenna based channel measurement system is designed and used to perform a series of narrow-beam channel measurements at 3.35 GHz in V2I highway scenarios. Data processing methods such as beam synthesis, angle estimation based on single-scattering assumption, and variational Bayesian Gaussian mixture model clustering are applied to extract parameters of multipath components and clusters. Then, we propose a cluster-based dynamic narrow-beam channel model, which considers the directional attenuation of narrow-beam and non-stationarity of clusters. In this model, the parameters of spatial-temporal characteristics are divided into global-cluster parameters and scatterer-cluster parameters, and the statistical distribution of these parameters are studied and modeled. Finally, a simulation method for the proposed channel model is provided, and model validation results show a good match between measurements and simulations in terms of channel characteristics such as delay spread and angle spread. This model provides a better characterization of the V2I narrow-beam channel and will be useful for designing and evaluating V2I communication systems.

Accurate knowledge of non-stationary narrow-beam channel characteristics is the prerequisite of applying dynamic beamforming technology in high-mobility communication scenarios. This paper proposes a three-dimensional geometry-based stochastic model (GBSM) for non-stationary narrow-beam channels in high-mobility communication scenarios. In the proposed GBSM, the impact of antenna pattern on angular distribution and the birth-death process of clusters in space-time-frequency (STF) domain are considered. Statistical properties of the proposed model are derived, including STF correlation function and multi-link spatial cross-correlation function (CCF), and the corresponding numerical results are analyzed. Moreover, the proposed model is verified by channel measurements in two high-mobility communication scenarios, such as vehicle-to-infrastructure scenario and high-speed railway scenario, in terms of temporal autocorrelation function, frequency correlation function and multi-link spatial CCF. It shows a good agreement between the measurement and model results, which confirms the reliability of the proposed model.

In this study, we consider a multi-cell millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system with a mixed analog-to-digital converter (mixed-ADC) and hybrid beamforming architecture, in which antenna selection is applied to achieve intelligent assignment of high- and low-resolution ADCs. Both exact and approximate closed-form expressions for the uplink achievable rate are derived in the case of maximum-ratio combining reception. The impacts on the achievable rate of user transmit power, number of radio frequency chains at a base station, ratio of high-resolution ADCs, number of propagation paths, and number of quantization bits are analyzed. It is shown that the user transmit power can be scaled down inversely proportional to the number of antennas at the base station. We propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, the energy efficiency is investigated, and an optimal tradeoff between the achievable rate and power consumption is discussed. Our results will provide a useful reference for the study of mixed-ADC multi-cell mmWave massive MIMO systems with antenna selection.

Coordinated multipoint (CoMP) and massive multiple-input multiple-output (mMIMO) are two of promising technologies in future intelligent high-speed railway (HSR) communication systems, whose performance is fundamentally determined by the characteristics of cooperative mMIMO channels. This article proposes a dynamic three-dimensional (3-D) wideband geometry-based stochastic model (GBSM) for HSR cooperative mMIMO channels. The proposed GBSM employs a sphere model and two elliptic-cylinder models to describe common and uncommon clusters for different links, and integrates the dynamic cluster evolution in both time and array domains. Statistical properties of the cooperative mMIMO model are investigated, such as multi-link spatial cross-correlation function and sum rate capacity. A corresponding dynamic 3-D wideband simulation model for the HSR cooperative mMIMO channel is also proposed. Finally, numerical results of the statistical properties are analyzed, and the proposed model is validated by realistic HSR channel measurement data, in terms of dual-link spatial cross-correlation and channel capacity. This model is more practical and will be helpful for facilitating the design and performance evaluation of future intelligent HSR communication systems.