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The lack of an efficient preamble detection algorithm remains a challenge for solving preamble collision problems in intelligent massive random access. To address this, the paper presents a novel early preamble detection scheme based on a maximum likelihood estimation (MLE) model.
A new modified Hadamard transform (MHT) is developed to separate high-frequencies from important components using a second-order derivative filter. This MHT is then used to design an efficient block MHT layer that eliminates noise and mitigates the vanishing gradients problem in the SVGD-based detectors.
A new blind normalized SVGD algorithm is derived to perform preamble detection without prior knowledge of noise power and the number of active devices. This makes it feasible for implementation in practical communication scenarios.
Experimental results show the proposed block MHT layer outperforms other transform-based methods in terms of computation costs and denoising performance. The blind normalized SVGD algorithm also achieves higher preamble detection accuracy and throughput than other state-of-the-art detection methods.
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by Xin Zhu,Ahme... at arxiv.org 03-29-2024
https://arxiv.org/pdf/2403.18846.pdfDeeper Inquiries