Proactive Doppler Shift Compensation in Vehicular Cyber-Physical Systems

Abstract

In vehicular cyber-physical systems (CPS), safety information, including vehicular speed and location information, is shared among vehicles via wireless waves at specific frequency. This helps control vehicle to alleviate traffic congestion and road accidents. However, Doppler shift existing between vehicles with high relative speed causes an apparent frequency shift for the received wireless wave, which consequently decreases the reliability of the recovered safety information and jeopardizes the safety of vehicular CPS. Passive confrontation of Doppler shift at the receiver side is not applicable due to multiple Doppler shifts at each receiver. In this paper, we provide a proactive Doppler shift compensation algorithm based on the probabilistic graphical model. Each vehicle pre-compensates its carrier frequency individually so that there is no frequency shift from the desired carrier frequency between each pair of transceiver. The pre-compensated offset for each vehicle is computed in a distributed fashion in order to be adaptive to the distributed and dynamic topology of vehicular CPS. Besides, the updating procedure is designed in a broadcasting fashion to reduce communication burden. It is rigorously proved that the proposed algorithm is convergence guaranteed even for systems with packet drops and random communication delays. Simulations based on real map and transportation data verify the accuracy and convergence property of the proposed algorithm. It is shown that this method achieves almost the optimal frequency compensation accuracy with an error approaching the Cramér-Rao lower bound.

Distributed Frequency Offsets Estimation

“…In practice, message exchange between agents may fail due to various factors, like separation distance, signal propagation environment, received signal strength, transmission power and modulation rate [21]. In the following experiments, different packet delivery ratio (PDR), which is the ratio of the number of packets successfully delivered to destination compared to the number of packets that have been sent out by the transmitter, is set to show the impacts of packet drop on proposed algorithms.…”

Distributed convergence verification for Gaussian belief propagation

“…At each round of message exchange, every variable node computes the outgoing messages to factor nodes according to (10) and (11). After receiving the messages from its neighboring variable nodes, each factor node computes its outgoing messages according to (7) and (8).…”

“…Gaussian belief propagation (BP) [1,2] provides an efficiently distributed way to compute the marginal distribution of unknown variables with a joint Gaussian distribution, and Gaussian BP has been adopted in a variety of topics including image denosing [3], distributed power state estimation [4] in smart grid, data detection for massive MIMO systems [5], distributed beamforming [6] and synchronization [7][8][9][10] in distributed networks, fast solver for system of linear equations [11], distributed rate control in ad-hoc networks [12], factor analyzer networks [13], sparse Bayesian learning [14], inter-cell interference mitigation [15], and peer-to-peer rating in social networks [16]. It has been shown that Gaussian BP computes the optimal centralized estimator if it converges [1,2].…”

System Structure and Network Computing Architecture of Petrochemical Cyber‐Physical System: Overview and Perspective

“…Through effective utilization of computation, communication, and control (3 C) technology, CPS can realize information processing, real‐time communication, and autonomous control in a highly integrated and interactive network environment . It focuses on the reasonable and effective allocation of system resources, as well as the optimization of system performance and efficiency, and provides personalized and humanized service for users …”