Toward Autonomous Driving With Short-Packet Rate Splitting: Age of Information Analysis and Optimization

Abstract

To address the high mobility impacts and the ultra-reliable and low-latency communications (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.