Deep Learning-Based Wideband Spectrum Sensing: A Low Computational Complexity Approach-Reference-Cited by-同舟云学术

Deep Learning-Based Wideband Spectrum Sensing: A Low Computational Complexity Approach

Published:2023-10 Issue:10 Volume:27 Page:2633-2637
ISSN:1089-7798
Container-title:IEEE Communications Letters
language:
Short-container-title:IEEE Commun. Lett.

Author:

Mei Ruru¹^ORCID,Wang Zhugang²^ORCID

Affiliation:

1. Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing, China

2. National Space Science Center, Chinese Academy of Sciences, Beijing, China

Funder

Strategic Priority Research Program of the Chinese Academy of Sciences (Class A)

Sub-Subject Number

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/4234/10275739/10236565.pdf?arnumber=10236565

Reference18 articles.

1. Deep learning based cooperative spectrum sensing with crowd sensors using data cleansing algorithm;rao;Proc Int Conf Edge Comput Appl (ICECAA),2022

2. Deep Learning for Satellites Based Spectrum Sensing Systems: A Low Computational Complexity Perspective

3. Spectrum Sensing in Interference and Noise Using Deep Learning

4. Deep Learning for Spectrum Sensing

5. Deep Learning-Based Spectrum Sensing in Cognitive Radio: A CNN-LSTM Approach

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Analysis and Optimization of Cooperative Jammer for Spatial Electromagnetic Spectrum Holes;2024 6th Global Power, Energy and Communication Conference (GPECOM);2024-06-04

2. Compressed Spectrum Sensing of Sparse Wideband Signals Based on Deep Learning;IEEE Transactions on Vehicular Technology;2024-06

3. Model Order Selection and Meta Analysis-Based Cooperative Wideband Spectrum Sensing;IEEE Communications Letters;2024-05