EKF-Based Joint Channel Estimation and Decoding Design for Non-Stationary OFDM Channel

Cyber-physical systems (CPSs) are characterized by integrating computation, communication, and physical system. In typical CPS application scenarios, vehicle-to-vehicle (V2V) and Industry Internet of Things (IIoT), due to doubly selective fading and non-stationary channel characteristics, the robust and reliable end-to-end communication is extremely important. Channel estimation is a major signal processing technology to ensure robust and reliable communication. However, the existing channel estimation methods for V2V and IIoT cannot effectively reduce intercarrier interference (ICI) and lower the computation complexity, thus leading to poor robustness. Aiming at this challenge, according to the channel characteristics of V2V and IIoT, we design two channel estimation methods based on the Bayesian filter to promote the robustness and reliability of end-to-end communication. For the channels with doubly selective fading and non-stationary characteristics of V2V and IIoT scenarios, in the one hand, basis extended model (BEM) is used to further reduce the complexity of the channel estimation algorithm under the premise that ICI can be eliminated in the channel estimation. On the other hand, aiming at the non-stationary channel, a channel estimation and interpolation method based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) Bayesian filters to jointly estimate the channel impulse response (CIR) and time-varying time domain autocorrelation coefficient is adopted. Through the MATLAB simulation, the robustness and reliability of end-to-end communication for V2V and IIoT are promoted by the proposed algorithms.

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“…According to [13], assuming a random walk model for r i , the state space model can be constructed as ⎧ ⎨ ⎩…”

Section: State Space Modelmentioning

confidence: 99%

“…In our previous research [13], a non-stationary channel estimation method based on extended Kalman filter (EKF) is proposed. However, the method is limited by its model and cannot effectively deal with ICI.…”

Section: Introductionmentioning

confidence: 99%

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

Yong

Shen

Sun

et al. 2019

J Wireless Com Network

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“…Notice that the Kalman filter is a well known solution to the problem of channel tracking [22]- [27]. In [22], Iltis addresses the problem of delay estimation in the presence of multipath using the EKF.…”

Section: Introductionmentioning

confidence: 99%

“…In [25], Simon et al proposes a state-space approach that jointly estimates the multipath Rayleigh channel gains and the carrier frequency offset (CFO), and in [26] they propose a soft-Kalman filter. Similar approaches based on iterative detection and decoding are proposed in [27]- [29]. These use, nevertheless, channel coding which prevents a direct comparison with our solution.…”

Section: Introductionmentioning

confidence: 99%

“…A central element that distinguishes this work from the previous ones is the state-transition model. In fact, we see that in [22]- [27] the multipath complex channel gains are approximated by the basis expansion model (BEM) with an auto-regressive (AR) process used to characterize the variations of the weighting coefficients of the BEM across the frame. Differently, we assume that amplitude of the complex channel gains is static and that only the phase is variable.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Joint Channel Equalization and Tracking for V2X Communications Using SC-FDE Schemes

et al. 2020

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Our aim with this paper is to present a solution suitable for vehicle-to-everything (V2X) communications, particularly, when employing single-carrier modulations combined with frequency-domain equalization (SC-FDE). In fact, we consider the V2X channel to be doubly-selective, where the variation of the channel in time is due to the presence of a Doppler term. Accordingly, the equalization procedure is dealt by a low-complexity iterative frequency-domain equalizer based on the iterative block decisionfeedback equalization (IB-DFE) while the tracking procedure is conducted employing an extended Kalman filter (EKF). The proposed system is very efficient since it allows a very low density of training symbols, even for fast-varying channels. Furthermore only two training symbols are required to initialize the tracking procedure. Thus, ensuring low latency together with reduced channel estimation overheads. INDEX TERMS Adaptive equalizers, channel estimation, digital communication, Kalman filters, signal processing, signal processing algorithms, vehicular and wireless technologies.

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Channel Tracking and Equalization for UAV Communications

Pedrosa

Dinis

Castanheira

et al. 2022

Integration of Unmanned Aerial Vehicles in Wireless Communication and Networks

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EKF-Based Joint Channel Estimation and Decoding Design for Non-Stationary OFDM Channel

Cited by 16 publications

References 14 publications

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

Efficient Joint Channel Equalization and Tracking for V2X Communications Using SC-FDE Schemes

Channel Tracking and Equalization for UAV Communications

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