The content introduces innovative frameworks for coherent and non-coherent schemes in grant-free NOMA systems. It discusses the challenges faced by traditional techniques and highlights the advantages of the proposed deep learning-assisted parallel interference cancellation approach. The simulations demonstrate superior performance over existing methods.
The paper proposes three frameworks based on parallel interference cancellation enhanced with deep learning for grant-free NOMA systems. These frameworks aim to improve activity detection, channel estimation, and data detection in both coherent and non-coherent schemes. By integrating deep learning into the process, the proposed approach shows enhanced performance and lower computational complexity compared to traditional techniques.
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by Yongjeong Oh... pada arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07255.pdfPertanyaan yang Lebih Dalam