Communication modulation signal recognition based on the deep multi-hop neural network

Wang, Yan; Lu, Qian; Jin, Yiheng; Zhang, Hao

doi:10.1016/j.jfranklin.2021.06.013

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…Embedded DSP processor is specially used for signal processing applications, such as digital filtering, spectrum analysis, and other instruments. e embedded system-ona-chip is an integrated device that pursues the most inclusive product system; its biggest feature is that it realizes the seamless combination of software and hardware and directly embeds the operating system code module in the processor chip, which is highly comprehensive, and a complex system can be realized by using hardware description languages such as VHDL inside the chip [20].…”

Section: Selection Of System Hardwarementioning

confidence: 99%

Secure Internet of Things Gateway Technology Based on Multicommunication Methods

Zhang

2022

Security and Communication Networks

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In order to explore how the Internet of Things implements a secure Internet of Things gateway technology, the author proposes a research on a secure Internet of Things gateway technology based on multicommunication methods. This method recommends key technical problems and solutions based on information represented by multiple communication methods and explores how the Internet of Things can realize the research of Internet of Things gateway technology. Research has shown that the security IoT gateway based on multiple communication methods is about 40% more efficient than the traditional method. By studying some exploratory guidance and suggestions for the development of the Internet of Things, it is found that there are still many problems to be solved before realizing the real Internet of Things environment.

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Section: Selection Of System Hardwarementioning

confidence: 99%

Secure Internet of Things Gateway Technology Based on Multicommunication Methods

Zhang

2022

Security and Communication Networks

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“…Due to the high computational complexity of the optimal solution, the calculation method was optimized and the results showed that the power coverage could be improved. Wang et al [12] used deep CNN to learn time domain signal features of different lengths to improve the modulation of traditional methods for fast and accurate signal identification, and also to solve the limitation of fixed length of input data in traditional DLM. The experiments showed that the recognition effect under this network better than the traditional network.…”

Section: Related Workmentioning

confidence: 99%

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

Juan Wang

2023

Journal of Computers

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<p>With the maturity and development of new generation communication technology, the demand for wireless network communication quality is getting higher and higher. However, the existing wireless network data transmission models are greatly affected by the signal quality in the data transmission process, which seriously affects the quality of data transmission. To address the problem of signal perturbation affecting communication quality in wireless network communication, this study designs an improved perturbation compression reconstruction algorithm that can be used to process block-structured data based on the block sparse structure in the original signal, and constructs a single-user wireless network communication data transmission model based on this algorithm. The performance simulation test of the model shows that the MSE values of the model are lower than those of the conventional and perfect schemes used as comparisons under the same noise conditions and the transmitted compressed data length conditions. When the variance of Gaussian white noise is 0.125, the MSEs of the research design model, the conventional scheme and the perfect scheme are 0.031, 0.048, and 0.047, respectively. Experimental data show that the improved disturbance compressed sensing reconstruction algorithm can better reconstruct the compressed information and reduce the information loss caused by the disturbance in the compression process on the premise of removing the signal disturbance. This research result has reference significance for improving the signal quality of wireless network communication.</p> <p> </p>

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“…[2]. Therefore, AMR technology has been widely researched by domestic and foreign scholars [3]. AMR methods can be divided into two types: likelihood estimation-based methods [4] and feature extraction-based methods [5].…”

Section: Introductionmentioning

confidence: 99%

Modulation recognition of communication signals based on cascade network

Hou,

Liu

2024

IEICE Trans. Commun.

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To improve the recognition rate of the end-to-end modulation recognition method based on deep learning, a modulation recognition method of communication signals based on a cascade network is proposed, which is composed of two networks: Stacked Denoising Auto Encoder (SDAE) network and DCELDNN(Dilated Convolution, ECA Mechanism, Long Short-Term Memory, Deep Neural Networks) network. SDAE network is used to denoise the data, reconstruct the input data through encoding and decoding, and extract deep information from the data. DCELDNN network is constructed based on the CLDNN (Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks) network. In the DCELDNN network, dilated convolution is used instead of normal convolution to enlarge the receptive field and extract signal features, the Efficient Channel Attention (ECA) mechanism is introduced to enhance the expression ability of the features, the feature vector information is integrated by a Global Average Pooling (GAP) layer, and signal features are extracted by the DCELDNN network efficiently. Finally, end-to-end classification recognition of communication signals is realized. The test results on the RadioML2018.01a dataset show that the average recognition accuracy of the proposed method reaches 63.1% at SNR of -10 to 15dB, compared with CNN, LSTM, and CLDNN models, the recognition accuracy is improved by 25.8%, 12.3%, and 4.8% respectively at 10dB SNR.

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Communication modulation signal recognition based on the deep multi-hop neural network

Cited by 11 publications

References 16 publications

Secure Internet of Things Gateway Technology Based on Multicommunication Methods

Secure Internet of Things Gateway Technology Based on Multicommunication Methods

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

Modulation recognition of communication signals based on cascade network

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