Zobrazeno 1 - 10
of 1 594
pro vyhledávání: '"Modulation recognition"'
Publikováno v:
Alexandria Engineering Journal, Vol 104, Iss , Pp 162-170 (2024)
Automatic modulation recognition (AMR) stands as a pivotal operation within industrial cognitive radio systems. State-of-the-art real-valued convolutional neural networks (CNNs) have innovated modulation recognition but view complex signal components
Externí odkaz:
https://doaj.org/article/f5ce4054512b44a7a2014bd9856955d3
Publikováno v:
Tongxin xuebao, Vol 45, Pp 14-25 (2024)
Considering the lack of a general multi-carrier signal dataset in urban multipath channels, and the challenge of recognizing the modulation types of distorted signals at low signal-to-noise ratio (SNR), a differentiable architecture search-based (DAR
Externí odkaz:
https://doaj.org/article/b94317970cfc4419b468a5c279d2328c
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract The modulation recognition technology for acoustic signals holds significant research importance in signal demodulation and communication signal reconnaissance, serving as a crucial component and key aspect. This paper investigates the modul
Externí odkaz:
https://doaj.org/article/cab9f3cae37e417f9efa2d5498c0dd45
Publikováno v:
Journal of Communications Software and Systems, Vol 20, Iss 2, Pp 198-205 (2024)
Automatic Digital Modulation Recognition (ADMR) is a critical component in modern communication systems, enabling efficient and flexible data transmission. This paper investigates the challenges associated with ADMR in scenarios where the received si
Externí odkaz:
https://doaj.org/article/96571df2d80c4139bceb101328b243f8
Autor:
CHENG Fengyun, ZHOU Jin
Publikováno v:
Dianxin kexue, Vol 40, Pp 139-150 (2024)
The existing modulation recognition algorithms based on deep learning theory require a large number of IQ signal samples during the training phase. It is difficult to obtain a large number of samples in complex electromagnetic environments, resulting
Externí odkaz:
https://doaj.org/article/273614ee947f425bbd660e18fea1493d
Autor:
Narges Rashvand, Kenneth Witham, Gabriel Maldonado, Vinit Katariya, Nishanth Marer Prabhu, Gunar Schirner, Hamed Tabkhi
Publikováno v:
IoT, Vol 5, Iss 2, Pp 212-226 (2024)
Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an innovative approach that leve
Externí odkaz:
https://doaj.org/article/4c6e82cc73e6437f9d2f190a3daa7942
Publikováno v:
Defence Technology, Vol 33, Iss , Pp 364-373 (2024)
Automatic modulation recognition (AMR) of radiation source signals is a research focus in the field of cognitive radio. However, the AMR of radiation source signals at low SNRs still faces a great challenge. Therefore, the AMR method of radiation sou
Externí odkaz:
https://doaj.org/article/1e8479ac0e9a4578a4c00fe3bdfe55a4
Publikováno v:
Tongxin xuebao, Vol 45, Pp 180-193 (2024)
In order to generate high-quality electromagnetic signal countermeasure examples, a fast Jacobian saliency map attack (FJSMA) method was proposed.The Jacobian matrix of the attack target class was calculated and feature saliency maps based on the mat
Externí odkaz:
https://doaj.org/article/074cc49d5daf421c8d7b461c7bb231bf
Publikováno v:
IEEE Access, Vol 12, Pp 121712-121722 (2024)
In the realms of Internet of Things (IoT), satellite communication, and related scenarios, automatic modulation recognition is crucial for accurate signal demodulation. In complex communication environments, accurately identifying diverse modulation
Externí odkaz:
https://doaj.org/article/3bed9f09067e4a0bb576eaea9866d607
Publikováno v:
IEEE Access, Vol 12, Pp 112558-112575 (2024)
In non-cooperative communication systems such as radio spectrum resource regulation and modern electronic warfare, automatic modulation recognition is a key technology. Traditional modulation recognition methods mainly rely on manual feature extracti
Externí odkaz:
https://doaj.org/article/0fd2da124a434e49aa27a5e8482639b2