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.

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Hyperloop is envisioned as a novel transportation way with merits of ultra-high velocity and great traveling comforts. In this paper, we present some concepts on the key technologies dedicated to the train-to-ground communication system based on some prevailing fifth-generation communication (5G) technologies from three aspects: wireless channel, network architecture, and resource management. First, we characterize the wireless channel of the distributed antenna system (DAS) using the propagation-graph channel modelling theory. Simulation reveals that a drastic Doppler shift variation appears when crossing the trackside antenna. Hence, the leaky waveguide system is a promising way to provide a stable receiving signal. In this regard, the radio coverage is briefly estimated. Second, a cloud architecture is utilized to integrate several successive trackside leaky waveguides into a logical cell to reduce the handover frequency. Moreover, based on a many-to-many mapping relationship between distributed units (DUs) and centralized units (CUs), a novel access network architecture is proposed to reduce the inevitable handover cost by using the graph theory. Simulation results show that this scheme can yield a low handover cost. Then, with regards to the ultra-reliable and low latency communication (uRLLC) traffic, a physical resource block (PRB) multiplexing scheme considering the latency requirements of each traffic type is exploited. Simulation presents that this scheme can maximize the throughput of non-critical mission communication services while guaranteeing the requirements of uRLLC traffic. Finally, in terms of the non-critical mission communication services, two cache-based resource management strategies are proposed to boost the throughput and reduce the midhaul link burden by pre-fetching and post-uploading schemes. Simulation demonstrates that the cache-based schemes can boost the throughput dramatically.

Cloud radio access network (C-RAN) is considered as a promising architecture for 5G with advantages of green energy, convenient resources allocation. In this paper, we explore the feasibility of C-RAN for high-speed railway (HSR) scenarios. A novel phenomenon of group handover is defined in the extensively and densely distributed railway network and we present a resource migration cost with a closedform expression to depict the group handover. To reduce the cost, we propose a novel connection relationship between the remote radio head (RRH) and the baseband unit (BBU) pool. Based on this, we establish a flexible network so as to allocate the resource dynamically and formulate a graph by abstracting the RRH-BBU and BBU-BBU mapping relationship. Then the minimization of resource migration cost along the high-speed train (HST) routine is converted into the shortest path problem (SPP). By using the modified Floyd-Warshall algorithm, the SPP can be solved with high efficiency compared with the conventional algorithm. Finally, the simulation result shows that the proposed mechanism can decrease the resources migration cost significantly. INDEX TERMS Cloud radio access network, group handover, graph theory, high-speed railway communication, RRH-BBU mapping.

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