Min-Max Latency Optimization Based on Sensed Position State Information in Internet of Vehicles

TL;DR

This paper proposes power allocation algorithms for Internet of Vehicles that leverage dual-function radar communication signals to sense vehicle positions and minimize maximum communication delay under power and accuracy constraints.

Contribution

It introduces two iterative convex optimization algorithms for power allocation in IoV, optimizing delay while considering sensing accuracy and power limits.

Findings

Algorithms converge reliably in simulations.

Significant reduction in maximum delay compared to existing schemes.

Applicable to various scenarios with different complexity levels.

Abstract

The dual-function radar communication (DFRC) is an essential technology in Internet of Vehicles (IoV). Consider that the road-side unit (RSU) employs the DFRC signals to sense the vehicles' position state information (PSI), and communicates with the vehicles based on PSI. The objective of this paper is to minimize the maximum communication delay among all vehicles by considering the estimation accuracy constraint of the vehicles' PSI and the transmit power constraint of RSU. By leveraging convex optimization theory, two iterative power allocation algorithms are proposed with different complexities and applicable scenarios. Simulation results indicate that the proposed power allocation algorithm converges and can significantly reduce the maximum transmit delay among vehicles compared with other schemes.