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arXiv Computer Vision By Qifei Wang, Zhen Gao, Li Qiao, Ziwei Wan, De Mi, Dapeng Li, Ying Sun

Knowledge Distillation Driven Semantic NOMA with GAN Refinement for 6G Robotic Vehicle Networks

Read the original on arXiv Computer Vision →

The paper introduces KDG‑SemNOMA, a framework for 6G robotic vehicle networks that combines knowledge distillation and generative models to improve semantic communication over uplink non‑orthogonal multiple access (NOMA). It employs a ConvNeXt‑based deep joint source‑channel coding architecture with an enhanced attention feature module for dynamic channel adaptation, and uses an orthogonal teacher model to guide a NOMA student model via two‑stage knowledge distillation. A channel‑conditional GAN further refines the reconstructed images, yielding higher pixel‑level accuracy and perceptual fidelity on the FFHQ‑256 dataset compared to state‑of‑the‑art methods.

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