Channel Knowledge Empowered Finite-Blocklength Rate-Splitting Transmission for High-Mobility Autonomous Driving
2026-07-11 • Information Theory
Information Theory
AI summaryⓘ
The authors explore a way to improve communication for self-driving cars by using a technique called rate-splitting multiple access (RSMA), which helps send data reliably even when signals change quickly. They use a special map, called a channel knowledge map (CKM), that shows how signals behave depending on location to make RSMA work better. Their method focuses on ensuring fair data rates for users and performs better than other techniques, especially when cars move fast. They also find that the success of their method depends on having accurate information in the CKM.
rate-splitting multiple access (RSMA)finite-blocklength (FBL)channel knowledge map (CKM)channel state information (CSI)ultra-low latencyhigh mobilityautonomous drivingspace-division multiple access (SDMA)non-orthogonal multiple access (NOMA)ergodic rate
Authors
Yi Wang, Yingyang Chen, Feng Bai, Li Wang, Gang Feng
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emerged as a promising multi-user transmission framework, showing robustness in dynamic situations where imperfect and outdated channel state information (CSI) is prevalent.Moreover, the advanced sensing, localization, and on-board computation capabilities of autonomous driving vehicles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.