Ultra-Reliable and Robust Channel Estimation Using Basis Expansion Model-Based UKF-RTSS Scheme for V2V Systems

LIAO Yong; CHEN Ying

doi:10.11999/JEIT210239

Volume 44 Issue 5

May 2022

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LIAO Yong, CHEN Ying. Ultra-Reliable and Robust Channel Estimation Using Basis Expansion Model-Based UKF-RTSS Scheme for V2V Systems[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1792-1799. doi: 10.11999/JEIT210239

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LIAO Yong, CHEN Ying. Ultra-Reliable and Robust Channel Estimation Using Basis Expansion Model-Based UKF-RTSS Scheme for V2V Systems[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1792-1799. doi: 10.11999/JEIT210239

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Ultra-Reliable and Robust Channel Estimation Using Basis Expansion Model-Based UKF-RTSS Scheme for V2V Systems

doi: 10.11999/JEIT210239

LIAO Yong^,,
CHEN Ying

School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

Funds: The National Natural Science Foundation of China (61501066), The Natural Science Foundation of Chongqing (cstc2019jcyj-msxmX0017)

Received Date: 2021-03-23
Accepted Date: 2021-11-05
Rev Recd Date: 2021-05-17

Available Online: 2021-11-13

Publish Date: 2022-05-25

Abstract

Abstract

The Internet of vehicles application scenarios put forward higher requirements for wireless communication in terms of bandwidth, delay, and reliability, especially in the Vehicle to Vehicle (V2V) communication scenario. For the technical challenges of channel estimation caused by time/frequency domain selective fading (dual selection fading) and non-stationary characteristics in the V2V high-speed mobile scenario, this paper proposes a channel estimation method of BEM (Basis Expansion Model)-based UKF-RTSS (Unscented Kalman Filter-Rauch-Tung-Striebel Smoother). The BEM model is used to fit the time-varying channel, and the estimation of the channel parameters is converted into the estimation of the basis function coefficients; The unscented Kalman filter (UKF) algorithm is used to estimate jointly the channel impulse response and the time-varying time-domain autocorrelation coefficient at the data, tracking the fast time-varying channel response. In order to improve further the accuracy of channel estimation, RTSS is introduced to perform channel estimation and interpolation on the backward channel state information, and it forms a joint estimator with a "filtering and smoothing" structure with UKF. System simulation analysis shows that under different speed and time-varying conditions, the BEM-based UKF-RTSS channel estimation method has higher channel estimation accuracy and robustness than other classic methods, and is especially suitable for wireless communication in the Internet of vehicles scenarios.
- Vehicle to Vehicle(V2V),
- Channel estimation,
- Basis Expansion Model(BEM),
- Unscented Kalman Filter(UKF),
- Joint estimation

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References(18)

References

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