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

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

BEM-based UKF Channel Estimation for 5G-enabled V2V Channel

An Unscented Kalman Filter (UKF) based on Basis Expansion Model (BEM) is proposed in this paper to cope with the challenges of 5G-enabled V2V channel estimation. The BEM is adopted to reduce the estimation complexity and eliminate the inter-carrier interference (ICI). A channel estimation based on UKF which is able to jointly estimate the time-varying time correlation coefficients and channel impulse response (CIR) in a non-linear state space model is proposed. Simulation results illustrate that the proposed BEM-based UKF method shows better estimation accuracy, robustness and bit error rate (BER) performance than the traditional channel estimation methods in 5G-enabled V2V channel

BEM-based EKF-RTSS Channel Estimation for Non-stationary Doubly-selective Channel

An extended Kalman filter and Rauch-Tung-Striebel Smoother (EKF-RTSS) based on Basis Expansion Model (BEM) is proposed in this paper to cope with the challenges of doubly-selective and non-stationary channel in high-speed environments. For doubly-selective channel, the BEM is adopted to reduce the estimation complexity. For non-stationary channel, a channel estimation based on EKF which is able to jointly estimate the time-varying time correlation coefficients and channel impulse response (CIR) is proposed. For further improving the channel estimation accuracy, a `filtering and smoothing' channel estimator structure is designed by introducing the RTSS into channel estimation and interpolation. Simulation results illustrate that the proposed BEM-based EKF-RTSS method show better estimation accuracy, robustness and bit error rate (BER) performance than the traditional methods in high-speed scenarios

Joint channel estimation and decoding design for 5G-enabled V2V channel

This paper addresses the problem of channel estimation in 5G-enabled vehicular-to-vehicular (V2V) channels with high-mobility environments and non-stationary feature. Considering orthogonal frequency division multiplexing (OFDM) system, we perform extended Kalman filter (EKF) for channel estimation in conjunction with Iterative Detector & Decoder (IDD) at the receiver to improve the estimation accuracy. The EKF is proposed for jointly estimating the channel frequency response and the time-varying time correlation coefficients. And the IDD structure is adopted to reduce the estimation errors in EKF. The simulation results show that, compared with traditional methods, the proposed method effectively promotes the system performance