Disturbance Signal Recognition Using Convolutional Neural Network for DAS System

Cheng, Quan; Yin, Yixin; Gui, Xin

doi:10.1109/icmtma52658.2021.00066

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…At this stage, with the rapid development of network information and wireless communication technology, within a prescribed distance, the IoT built by a large number of nodes has attracted wide attention from people in related fields, and the number of network devices and sensors deployed in the physical environment is rapidly increasing. The increase also brings new challenges to the wireless SMS and recognition, and the research on the distributed network architecture [16] of the combination of multiple receivers arises at the historic moment. Distributed multisensor node wireless SMSR technology can be divided into data layer-based fusion, feature layer-based fusion, and decision-making layer-based fusion schemes.…”

Section: Introductionmentioning

confidence: 99%

Research on Distributed Multisensor Spectrum Semantic Sensing and Recognition in Internet of Things Environment

Liu

Zhang

Liu³

et al. 2022

Wireless Communications and Mobile Computing

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The development of 5G technology has brought about a new era of Internet of Thing (IoT), and at the same time, electromagnetic spectrum monitoring and sensing have also ushered in huge challenges. Digital modulation recognition technology is an important content of electromagnetic spectrum sensing. In the increasingly complex wireless communication transmission environment, especially in noncooperative communication, it becomes more and more difficult to receive target signals and accurately extract effective semantic information from diverse modulation signals of the electromagnetic spectrum. At this stage, with the rapid development of network information and wireless communication technology, within a prescribed distance, the IoT built by many sensors has attracted wide attention from people in related fields. This paper proposes a distributed collaborative sensing spectrum semantic recognition architecture for communication signals based on feature fusion. Perform wireless communication and transmission between multiple sensors to form a self-organizing network to cooperatively sense signal semantic information, and extract the signal features of each sensor in the distributed network structure. Finally, the extracted sensor features are semantically analyzed and modeled, and the effective features are fused to complete the entire perception and recognition process. Even if the channel environment of a small number of receiving nodes deteriorates in a complex transmission environment, the signal quality features can still be accurately extracted, the classification and recognition effect like or higher than the best channel state performance can be achieved, and the fault tolerance of the system can be effectively improved. It can also enhance the performance of spectral semantic information sensing and recognition in the IoT environment.

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Section: Introductionmentioning

confidence: 99%