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Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication
March 13, 2024, 4:42 a.m. | Yongjeong Oh, Jaehong Jo, Byonghyo Shim, Yo-Seb Jeon
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we present a novel approach for joint activity detection (AD), channel estimation (CE), and data detection (DD) in uplink grant-free non-orthogonal multiple access (NOMA) systems. Our approach employs an iterative and parallel interference removal strategy inspired by parallel interference cancellation (PIC), enhanced with deep learning to jointly tackle the AD, CE, and DD problems. Based on this approach, we develop three PIC frameworks, each of which is designed for either coherent or …
abstract arxiv communication cs.ai cs.lg data deep learning detection eess.sp free grant interference iterative machine multiple novel paper strategy systems type
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