Towards Autonomous Driving with Short-Packet Rate Splitting: Age of Information Analysis and Optimization

TL;DR

This paper explores short-packet rate splitting with age of information analysis to improve data freshness in autonomous driving, optimizing communication strategies for low latency and reliability.

Contribution

It introduces a novel short-packet rate splitting scheme with a closed-form AAoI analysis and an optimization algorithm tailored for autonomous driving communication needs.

Findings

The proposed scheme significantly reduces AAoI compared to traditional methods.

The multi-start SCA algorithm effectively balances power, rate splitting, and QoS constraints.

Simulation results confirm improved system fairness and ultra-low AAoI performance.

Abstract

To address the high mobility impacts and the ultra-reliable and low-latency communication (URLLC) requirements in autonomous driving scenarios, rate-splitting multiple access (RSMA) combined with short-packet communication (SPC) emerges as a promising solution.Autonomous vehicles rely on real-time information exchange to ensure safety and coordination, making information freshness essential.By jointly capturing transmission delays and packet errors, age of information (AoI) serves as a comprehensive metric for freshness.In this paper, we investigate short-packet rate splitting to enhance information freshness measured by the AoI.By splitting the unicast messages into common and private parts, encoding all common parts together with the multicast message into a common stream, and encoding each private part into a private stream, RSMA effectively manages interference and enables achieving lower AoI.By considering critical factors such as transmit power, vehicle velocity, blocklength, and the number of transmit antennas, we derive closed-form expressions for the average AoI (AAoI) of the common stream under partial decoding and the overall AAoI under complete decoding.To enhance the AAoI performance, we propose the multi-start two-step successive convex approximation (SCA) algorithm.This algorithm first optimizes the power allocation and subsequently optimizes the rate splitting under the quality of service (QoS) trade-off constraint.Simulation results demonstrate that our short-packet rate-splitting scheme significantly improves the AAoI performance while ensuring system fairness and enabling ultra-low AAoI through the common stream, meeting the requirements of autonomous driving applications.Moreover, the trade-off between the common and overall performance is revealed, indicating that the overall performance can be further enhanced while maintaining the advantages of the common stream.