Channel Knowledge Empowered Finite-Blocklength Rate-Splitting Transmission for High-Mobility Autonomous Driving

2026-07-11

AI Summary

This work addresses the performance degradation of extreme ultra-reliable low-latency communication (xURLLC) in highly dynamic autonomous driving scenarios, caused by imperfect and rapidly time-varying channel state information. To tackle this challenge, it introduces— for the first time—a data-driven channel knowledge map (CKM) into a finite blocklength rate-splitting multiple access (FBL-RSMA) framework. By integrating large-scale channel characteristics correlated with vehicle positions, the study proposes a refined rate-splitting mechanism tailored to guarantee minimum user rates and derives a tight closed-form bound on the ergodic rate of private streams to optimize resource allocation for the common stream. Experimental results demonstrate that the proposed approach significantly outperforms SDMA and NOMA, and that CKM enables more accurate acquisition of large-scale channel information than model-driven methods, thereby enhancing system reliability, fairness, and robustness against channel estimation errors.

Abstract

To meet the extended ultra-low latency and high reliability (xURLLC) requirements for autonomous driving systems, multiple access schemes must operate reliably in high-mobility and complex propagation environments. Recently, rate-splitting multiple access (RSMA) has emerged as a promising multi-user transmission framework, showing robustness in dynamic situations where imperfect and outdated channel state information (CSI) is prevalent.Moreover, the advanced sensing, localization, and on-board computation capabilities of autonomous driving vehicles facilitate the construction of a channel knowledge map (CKM), which is a key enabler for environment-aware communications in future 6G networks.In this work, we propose a CKM empowered finite-blocklength (FBL) RSMA for downlink autonomous driving system. The location-dependent large-scale channel information provided by CKM is exploited in RSMA to develop a refined rate-splitting design. The min-rate performance of FBL rate splitting is analyzed explicitly to ensure user fairness. We derive a new and tight closed-form bound for the private-stream ergodic rate. Combined with the closed-form common-stream expression, an efficient optimization design of rate-splitting ratios has been formulated. Numerical results show that the CKM empowered FBL RSMA outperforms space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA), particularly in high-mobility scenarios. Its performance is improved by a data-based CKM, which provides more accurate large-scale channel information than model-based approaches and enables more precise common-stream allocation. The results also reveal that RSMA is sensitive to errors in large-scale channel knowledge, emphasizing the importance of accurate CKM information for optimal rate-splitting.

Problem

• xURLLC
• high-mobility
• channel state information
• autonomous driving
• multiple access

Innovation

• Channel Knowledge Map (CKM)
• Finite-Blocklength (FBL)
• Rate-Splitting Multiple Access (RSMA)
• High-Mobility Communications
• xURLLC