This paper considers channel estimation and system performance for the uplink of a single-cell massive multiple-input multiple-output (MIMO) system. Each receive antenna of the base station (BS) is assumed to be equipped with a pair of onebit analog-to-digital converters (ADCs) to quantize the real and imaginary part of the received signal. We first propose an approach for channel estimation that is applicable for both flat and frequency-selective fading, based on the Bussgang decomposition that reformulates the nonlinear quantizer as a linear function with identical first-and second-order statistics. The resulting channel estimator outperforms previously proposed approaches across all SNRs. We then derive closed-form expressions for the achievable rate in flat fading channels assuming low SNR and a large number of users for the maximal ratio and zero forcing receivers that takes channel estimation error due to both noise and one-bit quantization into account. The closed-form expressions in turn allow us to obtain insight into important system design issues such as optimal resource allocation, maximal sum spectral efficiency, overall energy efficiency, and number of antennas. Numerical results are presented to verify our analytical results and demonstrate the benefit of optimizing system performance accordingly.

The widespread popularity of high-speed railways (HSRs) urges a critical demand on highdata-rate railway communication services for both train operation and passenger experience. To satisfy the ever-increasing requirements, future HSR communication systems, such as long-term evolution for railway (LTE-R), fifth generation (5G) on HSR, and 5G for railway (5G-R), and corresponding transmission technologies, e.g., mobile relay, coordinated multipoint, massive multiple-input multiple-output (MIMO), and millimeter-wave (mmWave), have recently attracted much attention. Radio channel modeling is the foundation of design and evaluation of wireless systems and transmission technologies. This paper focuses on a survey of channel modeling for the future HSR communication systems. The significant requirements of future HSR channel models are highlighted, and recent advances in the HSR channel modeling are reviewed. Finally, potential research directions for future HSR channel modeling are outlined.INDEX TERMS 5G, high-speed railway communications, channel modeling, multi-link, massive MIMO, millimeter-wave, nonstationarity, clustering, and deep learning.

No abstract

Wireless channel scenarios identification is of pivotal significance for dedicated wireless communication design, especially for the heterogeneous network covering rich propagation environments. In this paper, the identification problem is investigated by machine learning approaches. To enhance the identification performance, some preprocessing methods, mainly referring to the data normalization and dimension reduction, are adopted. Then, both supervised and unsupervised learning algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), k-means, and Gaussian mixture model (GMM) are used to realize the scenarios identification, respectively. Finally, the identification performance of these four approaches are validated both on the actual measured HSR wireless channel data sets and the QuaDRiGa channel emulation platform with the ability of multiple scenarios emulation. Most of the results indicate that k-NN and SVM approaches can achieve an accuracy over 90%. As for those two unsupervised learning approaches, the GMM proves to be a promising approach by presenting a performance close to the former two approaches without training process, whereas the k-means yields an accuracy about 80%. INDEX TERMS Wireless channel, scenarios identification, machine learning, QuaDRiGa platform, highspeed railway scenarios.