Abstract
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.
Highlights
Cyber-physical systems (CPSs) are multidimensional complex systems with real-time perception, dynamic control, and information services, which consist of comprehensive computing, networking, and physical environments to implement information integration and deep collaboration using computing, communication, and control technologies (3Cs) [1,2,3]
6.1 Simulation results in V2V In order to demonstate the improvement in robustness and reliability provided by channel estimation methods proposed in this paper, basis extended model (BEM)-extended Kalman filter (EKF) and BEM-unscented Kalman filter (UKF), in endto-end communication for CPS, we mainly present the performance of them in V2V and Industry Internet of Things (IIoT) which are the main environments for CPS
Since we believe that the channel estimation methods with higher estimation accuracy and lower bit error rate (BER) in different velocities could improve the robustness for end-to-end communication in CPS
Summary
Cyber-physical systems (CPSs) are multidimensional complex systems with real-time perception, dynamic control, and information services, which consist of comprehensive computing, networking, and physical environments to implement information integration and deep collaboration using computing, communication, and control technologies (3Cs) [1,2,3]. CPS realizes the integrated design of computing, communication, and physical system, which can make the system more reliable and high efficient and realize real-time collaboration. It has broad application prospects [4,5,6]. For the IIoT scenario shown, due to the influence of various scatters in the factory, the number of taps of the channel is time-varying [8, 11] It means that the transmission path of the wireless channel includes a direct path and scatter paths and dynamic paths. In the communication scenario of IIoT, since the scatters in the factory are very rich and in moving states, the channel will show the doubly selective fading characteristics
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