Radio communications is one of the most disruptive technologies in railways, enabling a huge set of value-added services that greatly improve many aspects of railways, making them more efficient, safer, and profitable. Lately, some major technologies like ERTMS for high-speed railways and CBTC for subways have made possible a reduction of headway and increased safety never before seen in this field. The railway industry is now looking at wireless communications with great interest, and this can be seen in many projects around the world. Thus, railway radio communications is again a flourishing field, with a lot of research and many things to be done. This survey article explains both opportunities and challenges to be addressed by the railway sector in order to obtain all the possible benefits of the latest radio technologies.
Modern society demands cheap, more efficient, and safer public transport. These enhancements, especially an increase in efficiency and safety, are accompanied by huge amounts of data traffic that need to be handled by wireless communication systems. Hence, wireless communications inside and outside trains are key technologies to achieve these efficiency and safety goals for railway operators in a cost-efficient manner. This paper briefly describes nowadays used wireless technologies in the railway domain and points out possible directions for future wireless systems. Channel measurements and models for wireless propagation are surveyed and their suitability in railway environments is investigated. Identified gaps are pointed out and solutions to fill those gaps for wireless communication links in railway environments are proposed.
Abstract-This letter presents a deep insight on a real implementation of a train-to-wayside radio on subway tunnels that makes use of a 2 X 2 multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) setup. The main purpose of this letter is to study in detail the keyhole phenomenon of an MIMO-OFDM train-to-wayside communication system on a tunnel. MIMO keyholes are studied in different tunnels sections, and capacity results are provided. Moreover, we introduce the first keyhole measurements on a railway tunnel. Finally, we follow a quantitative approach to estimate keyhole probabilities on each tunnel stretch and capacity outage curves.
Wireless Big Data has aroused extensive attention, as mass mobile devices have been developed and deployed for the upcoming 5G era. The context information of these devices is of importance for personalized services in a smart environment. Nevertheless, the constant change of scenes challenges to the network operator. In this paper, we propose an ensemble learning scheme for indoor-outdoor classification for a typical urban area, based on the cellular data captured in a commercial LTE network. The variables are extracted by network key performance indicators (KPIs) and radio propagation knowledge. Based on these main variables, the decision trees grow and split by the Gini index of sampled features. Then, all decision trees are assembled as weak learners to build the ensemble scheme, thus improving the discrimination ability. The self-validation results show the ensemble model achieves extreme accurate (with an out-of-bag error lower than 1%) classification for indoor and outdoor environments. Moreover, the prominent variables are selected based on the variable importance of in the initial training. The reconfigured model based on fewer variables and less weak learners also gains the highest accuracy and relative short compute time, compared with other classical machine learning methods.INDEX TERMS Propagation measurement, ensemble learning, LTE, channel model, scene classification.
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