[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Liang et al., 2021 - Google Patents

Automatic modulation recognition based on adaptive attention mechanism and ResNeXt WSL model

Liang et al., 2021

Document ID
1284128951870715866
Author
Liang Z
Tao M
Wang L
Su J
Yang X
Publication year
Publication venue
IEEE Communications Letters

External Links

Snippet

Automatic modulation recognition (AMR) plays an important role in modern wireless communication. In this letter, a novel framework for AMR is proposed. The ResNeXt network serves as the backbone, and four proposed adaptive attention mechanism modules are …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; Arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks ; Receiver end arrangements for processing baseband signals
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L2025/03433—Arrangements for removing intersymbol interference characterised by equaliser structure
    • H04L2025/03439—Fixed structures
    • H04L2025/03445—Time domain
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; Arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks ; Receiver end arrangements for processing baseband signals
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03012—Arrangements for removing intersymbol interference operating in the time domain
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation

Similar Documents

Publication Publication Date Title
Liang et al. Automatic modulation recognition based on adaptive attention mechanism and ResNeXt WSL model
Meng et al. Automatic modulation classification: A deep learning enabled approach
Zhang et al. Automatic modulation classification using CNN-LSTM based dual-stream structure
Zhang et al. Overfitting and underfitting analysis for deep learning based end-to-end communication systems
CN112418014B (en) Modulated signal identification method based on wavelet transformation and convolution long-term and short-term memory neural network
Huang et al. Automatic modulation classification using contrastive fully convolutional network
Li et al. Robust automated VHF modulation recognition based on deep convolutional neural networks
Nie et al. Deep hierarchical network for automatic modulation classification
Zhang et al. Modulation recognition of underwater acoustic signals using deep hybrid neural networks
Zhang et al. A data preprocessing method for automatic modulation classification based on CNN
CN111968666B (en) Hearing aid voice enhancement method based on depth domain self-adaptive network
Wu et al. Automatic modulation classification based on deep learning for software‐defined radio
Sang et al. Application of novel architectures for modulation recognition
Yongshi et al. CNN-based modulation classification in the complicated communication channel
Yunhao et al. ConvLSTMAE: A spatiotemporal parallel autoencoders for automatic modulation classification
CN110311876A (en) The implementation method of underwater sound OFDM receiver based on deep neural network
Zhang et al. An Autoencoder-based I/Q channel interaction enhancement method for automatic modulation recognition
Qiu et al. Deepsig: A hybrid heterogeneous deep learning framework for radio signal classification
Usman et al. AMC-IoT: Automatic modulation classification using efficient convolutional neural networks for low powered IoT devices
Liang et al. Attention mechanism based ResNeXt network for automatic modulation classification
Zhang et al. Modulation recognition using wavelet-assisted convolutional neural network
Lei et al. Towards recurrent neural network with multi-path feature fusion for signal modulation recognition
Fan et al. Demodulator based on deep belief networks in communication system
Kalade et al. Using sequence to sequence learning for digital bpsk and qpsk demodulation
Lu et al. Dual-channel hybrid neural network for modulation recognition