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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Vydáno v:International Conference on Wireless Communications and Signal Processing s. 1 - 5
Hlavní autoři: Shi, Xiaowen, Ma, Yuxin, Liu, Luohui, Han, Yi
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 20.10.2021
Témata:
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
ISSN:2472-7628
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Shrnutí: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.
ISSN:2472-7628
DOI:10.1109/WCSP52459.2021.9613140