Knowledge Distillation Driven Semantic NOMA with GAN Refinement for 6G Robotic Vehicle Networks
To achieve sustainable intelligent mobility, 6G-empowered robotic vehicles (RVs) require high-fidelity visual perception under stringent bandwidth and energy constraints. Semantic communication offers a spectral-efficient solution but suffers from severe interference in uplink non-orthogonal multiple access (NOMA) RV networks. To address this, we propose a knowledge distillation-driven and generative models-enhanced NOMA framework for robust and green RV communications, named KDG-SemNOMA. First, we develop a ConvNeXt-based deep joint source-channel coding (DeepJSCC) architecture with an enhanc
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- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
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- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
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- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
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- LinkedLinked via arxiv author · 85%Qifei Wang →
“Knowledge Distillation Driven Semantic NOMA with GAN Refinement for 6G Robotic Vehicle Networks”
- LinkedLinked via arxiv author · 85%Zhen Gao →
“Knowledge Distillation Driven Semantic NOMA with GAN Refinement for 6G Robotic Vehicle Networks”
- LinkedLinked via arxiv author · 85%Li Qiao →
“Knowledge Distillation Driven Semantic NOMA with GAN Refinement for 6G Robotic Vehicle Networks”