Deep Learning-Based PRACH Detection Algorithm Design and Simulation - Details

Author：

Fang, Ruijie (Fang, Ruijie.) | Chen, Huamin (Chen, Huamin.) | Liu, Wei (Liu, Wei.)

Indexed by：

EI Scopus

Abstract：

PRACH　(Physical　random　access　channel)　plays　an　important　role　in　fifth　generation　(5G)　system,　and　PRACH　detection　is　a　key　issue　in　the　field　of　wireless　signal　processing.　The　work　in　this　paper　proposes　a　FNN　(Fully-connected　Neural　Network)　based　algorithm　to　reduce　the　signal　processing　complexity　without　loss　of　detection　rate.　Different　models　are　given　in　this　paper　to　support　different　lengths　of　PRACH　preambles.　Simulation　results　are　provided　to　evaluate　the　performance　of　the　proposed　algorithm.　©　2022　IEEE.

Keyword：

Signal detection Deep learning 5G mobile communication systems

Author Community：

[ 1 ] [Fang, Ruijie]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 2 ] [Chen, Huamin]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 3 ] [Liu, Wei]Beijing University of Technology, Faculty of Information Technology, Beijing, China

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Source ：

Year： 2022

Page： 505-511

Language： English

Cited Count：

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SCOPUS Cited Count： 2

ESI Highly Cited Papers on the List： 0 Unfold All

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Chinese Cited Count：

30 Days PV： 2

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