Full Text:   <372>

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CLC number: TN928

On-line Access: 2021-07-12

Received: 2020-09-30

Revision Accepted: 2021-01-21

Crosschecked: 2021-05-01

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Zhao Yi


Weixia Zou


Xuebin Sun


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.6 P.777-789


Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond

Author(s):  Zhao Yi, Weixia Zou, Xuebin Sun

Affiliation(s):  MOE Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China; more

Corresponding email(s):   yz17tx@bupt.edu.cn, zwx0218@bupt.edu.cn

Key Words:  Massive multiple-input multiple-output, Millimeter wave, Channel estimation, Vehicular communication, Time-varying

Zhao Yi, Weixia Zou, Xuebin Sun. Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(6): 777-789.

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T1 - Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond
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A1 - Xuebin Sun
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millimeter wave (mmWave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5G and beyond. To realize applications in future vehicular communications, it is important to take a robust mmWave vehicular network into consideration. However, one challenge in such a network is that mmWave should provide an ultra-fast and high-rate data exchange among vehicles or vehicle-to-infrastructure (V2I). Moreover, traditional real-time channel estimation strategies are unavailable because vehicle mobility leads to a fast variation mmWave channel. To overcome these issues, a channel estimation approach for mmWave V2I communications is proposed in this paper. Specifically, by considering a fast-moving vehicle secnario, a corresponding mathematical model for a fast time-varying channel is first established. Then, the temporal variation rule between the base station and each mobile user and the determined direction-of-arrival are used to predict the time-varying channel prior information (PI). Finally, by exploiting the PI and the characteristics of the channel, the time-varying channel is estimated. The simulation results show that the scheme in this paper outperforms traditional ones in both normalized mean square error and sum-rate performance in the mmWave time-varying vehicular system.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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