Autonomous Driving with RSMA-Enabled Finite Blocklength Transmissions: Ergodic Performance Analysis and Optimization Article Swipe
Yi Wang
,
Yingyang Chen
,
Li Wang
,
Donghong Cai
,
Xiaofan Li
,
Pingzhi Fan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2508.12012
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2508.12012
Rate-splitting multiple access (RSMA) is a key technology for next-generation multiple access systems due to its robustness against imperfect channel state information (CSI). This makes RSMA particularly suitable for high-mobility autonomous driving, where ultra-reliable and low-latency communication (URLLC) is essential. To address the stringent requirements, this study enables RSMA finite blocklength (FBL) transmissions and explicitly evaluates the ergodic performance. We derive the closed-form lower bound for the ergodic sum-rate of RSMA, considering vital factors such as the vehicle velocities, vehicle positions, power allocation of each stream, blocklengths, and block error rates (BLERs). To further enhance the ergodic sum-rate while complying with quality of service (QoS) rate constraints, we jointly optimize the global power coefficient, private power distribution, and common rate splitting. Guided by gradient descent, we first adjust the global power coefficient based on its sum-rate solution. This parameter regulates the power state of the common stream, allowing for dynamic activation or deactivation: if active, we optimize the private power distribution and adjust the common rate splitting to meet minimum transmission constraints; if inactive, we use the sequential quadratic programming for private power distribution optimization. Simulation results confirm that our RSMA scheme significantly improves the ergodic performance, reduces blocklength and BLER, surpassing the RSMA counterpart with average private power and space division multiple access (SDMA). Furthermore, our approach is validated to guarantee the rates for users with the poorest channel conditions, thereby enhancing fairness across the network.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2508.12012
- https://arxiv.org/pdf/2508.12012
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4414460050
All OpenAlex metadata
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https://openalex.org/W4414460050Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2508.12012Digital Object Identifier
- Title
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Autonomous Driving with RSMA-Enabled Finite Blocklength Transmissions: Ergodic Performance Analysis and OptimizationWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
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2025Year of publication
- Publication date
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2025-08-16Full publication date if available
- Authors
-
Yi Wang, Yingyang Chen, Li Wang, Donghong Cai, Xiaofan Li, Pingzhi FanList of authors in order
- Landing page
-
https://arxiv.org/abs/2508.12012Publisher landing page
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https://arxiv.org/pdf/2508.12012Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2508.12012Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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