Proactive Doppler Shift Compensation in Vehicular Cyber-Physical Systems

TL;DR

This paper introduces a proactive, distributed Doppler shift compensation algorithm for vehicular cyber-physical systems that improves safety communication reliability by pre-adjusting carrier frequencies, even under dynamic network conditions.

Contribution

It proposes a novel probabilistic graphical model-based algorithm for distributed Doppler compensation that guarantees convergence and adapts to network dynamics.

Findings

Achieves near-optimal frequency compensation accuracy.

Proven convergence even with packet drops and delays.

Validated with real map and transportation data.

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 Cram\'{e}r-Rao lower bound.