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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.20943 |
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| _version_ | 1866917219808575488 |
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| author | Zhang, Xuejian He, Ruisi Ai, Bo Yang, Mi Ding, Jianwen Gao, Shuaiqi Qi, Ziyi Zhang, Zhengyu Zhong, Zhangdui |
| author_facet | Zhang, Xuejian He, Ruisi Ai, Bo Yang, Mi Ding, Jianwen Gao, Shuaiqi Qi, Ziyi Zhang, Zhengyu Zhong, Zhangdui |
| contents | With the development of high-speed railways, 5G for Railways (5G-R) is gradually replacing Global System for the Mobile Communications for Railway (GSM-R) worldwide to meet increasing demands. The large bandwidth, array antennas, and non-stationarity caused by high mobility has made 5G-R channel characterization more complex. Therefore, it is essential to develop an accurate channel model for 5G-R. However, researches on channel characterization and time-variant models specific to 5G-R frequency bands and scenarios is scarce. There are virtually no cluster-based time-variant channel models that capture statistical properties of 5G-R channel. In this paper, we propose a cluster-based time-variant channel model for 5G-R within an enhanced 3GPP framework, which incorporates time evolution features. Extensive channel measurements are conducted on 5G-R private network test line in China. We then extract and analyze typical channel fading characteristics and multipath cluster characteristics. Furthermore, birth-death process of the clusters is modeled by using a four-state Markov chain. Finally, a generalized clustered delay line (CDL) model is established in accordance with 3GPP standard and validated by comparing the results of measurements and simulations. This work enhances the understanding of 5G-R channels and presents a flexible cluster-based time-variant channel model. The results can be used in the design, deployment, and optimization of 5G-R networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20943 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cluster-Based Time-Variant Channel Characterization and Modeling for 5G-Railways Zhang, Xuejian He, Ruisi Ai, Bo Yang, Mi Ding, Jianwen Gao, Shuaiqi Qi, Ziyi Zhang, Zhengyu Zhong, Zhangdui Information Theory With the development of high-speed railways, 5G for Railways (5G-R) is gradually replacing Global System for the Mobile Communications for Railway (GSM-R) worldwide to meet increasing demands. The large bandwidth, array antennas, and non-stationarity caused by high mobility has made 5G-R channel characterization more complex. Therefore, it is essential to develop an accurate channel model for 5G-R. However, researches on channel characterization and time-variant models specific to 5G-R frequency bands and scenarios is scarce. There are virtually no cluster-based time-variant channel models that capture statistical properties of 5G-R channel. In this paper, we propose a cluster-based time-variant channel model for 5G-R within an enhanced 3GPP framework, which incorporates time evolution features. Extensive channel measurements are conducted on 5G-R private network test line in China. We then extract and analyze typical channel fading characteristics and multipath cluster characteristics. Furthermore, birth-death process of the clusters is modeled by using a four-state Markov chain. Finally, a generalized clustered delay line (CDL) model is established in accordance with 3GPP standard and validated by comparing the results of measurements and simulations. This work enhances the understanding of 5G-R channels and presents a flexible cluster-based time-variant channel model. The results can be used in the design, deployment, and optimization of 5G-R networks. |
| title | Cluster-Based Time-Variant Channel Characterization and Modeling for 5G-Railways |
| topic | Information Theory |
| url | https://arxiv.org/abs/2412.20943 |