A Location-Aware hybrid beamforming system for High Speed Trains
High speed train (HST) has become one of the most popular ground transportations. Huge data streams are required to be transmitted to the base station (BS) for the HST. It is a particularly challenging problem due to the highly varying channels. Thanks to the development of 5G networks, millimeter-w...
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| Vydané v: | International Conference on Wireless Communications and Signal Processing s. 1 - 5 |
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| Hlavní autori: | , , , |
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| Jazyk: | English |
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IEEE
20.10.2021
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| ISSN: | 2472-7628 |
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| Abstract | High speed train (HST) has become one of the most popular ground transportations. Huge data streams are required to be transmitted to the base station (BS) for the HST. It is a particularly challenging problem due to the highly varying channels. Thanks to the development of 5G networks, millimeter-wave (mmWave) beamforming can be employed to meet the requirement of the HST services. In this work, a location-aware beamforming technique is proposed for HST. More specifically, we divide this technique into two stages, namely localization and data transmission. In the first stage, a deep learning-based localization method is developed to acquire the position of the carriages. Next, a hybrid precoding system is leveraged to realize beamforming with a much smaller number of radio frequency (RF) chains. In addition, the quality of service (QoS) of each carriage is considered by formulating a difference of two convex (D.C.) programming problems. Finally, extensive simulations and experiments are conducted to demonstrate the effectiveness of our proposed location-aware hybrid beamforming system. |
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| AbstractList | High speed train (HST) has become one of the most popular ground transportations. Huge data streams are required to be transmitted to the base station (BS) for the HST. It is a particularly challenging problem due to the highly varying channels. Thanks to the development of 5G networks, millimeter-wave (mmWave) beamforming can be employed to meet the requirement of the HST services. In this work, a location-aware beamforming technique is proposed for HST. More specifically, we divide this technique into two stages, namely localization and data transmission. In the first stage, a deep learning-based localization method is developed to acquire the position of the carriages. Next, a hybrid precoding system is leveraged to realize beamforming with a much smaller number of radio frequency (RF) chains. In addition, the quality of service (QoS) of each carriage is considered by formulating a difference of two convex (D.C.) programming problems. Finally, extensive simulations and experiments are conducted to demonstrate the effectiveness of our proposed location-aware hybrid beamforming system. |
| Author | Han, Yi Ma, Yuxin Liu, Luohui Shi, Xiaowen |
| Author_xml | – sequence: 1 givenname: Xiaowen surname: Shi fullname: Shi, Xiaowen organization: Casco Singal, Ltd,Shanghai,China,200436 – sequence: 2 givenname: Yuxin surname: Ma fullname: Ma, Yuxin organization: Casco Singal, Ltd,Shanghai,China,200436 – sequence: 3 givenname: Luohui surname: Liu fullname: Liu, Luohui organization: Casco Singal, Ltd,Shanghai,China,200436 – sequence: 4 givenname: Yi surname: Han fullname: Han, Yi email: hanyi@casco.com.cn organization: Casco Singal, Ltd,Shanghai,China,200436 |
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| Snippet | High speed train (HST) has become one of the most popular ground transportations. Huge data streams are required to be transmitted to the base station (BS) for... |
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| SubjectTerms | Array signal processing D.C. Programming High Speed Train Hybrid Beamforming Localization Location awareness Quality of service Radio frequency Radio transmitters Signal processing algorithms Wireless communication |
| Title | A Location-Aware hybrid beamforming system for High Speed Trains |
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