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

2026-04-15

AI Summary

This work addresses the stringent requirements of autonomous driving for ultra-reliable low-latency communication and high information freshness by proposing a novel integration of rate-splitting multiple access (RSMA) with short-packet communication (SPC). The approach uniquely combines RSMA and SPC to optimize the age of information (AoI) by splitting unicast messages into common and private parts and jointly encoding them. The study establishes a closed-form analytical model for the average AoI and develops a multi-start, two-stage successive convex approximation algorithm that significantly reduces average AoI while ensuring system fairness. This method effectively meets the dual challenges of ultra-low latency and high reliability inherent in autonomous driving scenarios.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Life-Long and Continual Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• autonomous driving
• ultra-reliable and low-latency communication
• age of information
• short-packet communication
• information freshness

Innovation

• Rate-Splitting Multiple Access
• Short-Packet Communication
• Age of Information
• Autonomous Driving
• Successive Convex Approximation