Modulation recognition of symbol shaped digital signals

Fu-qing, Huang; Zhang, Zhiming; Xu, Yitao; Ren, Guochun

doi:10.1109/icccas.2008.4657787

Cited by 9 publications

(6 citation statements)

References 4 publications

(10 reference statements)

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“…A/P Long Short Term Memory (LSTM) [16], a LSTM denoising auto-encoder [14] Well recognize AM-SSB, and distinguish between QAM16 and QAM64 [22] Spectrum RSBU-CW with Welch spectrum, square spectrum, and fourth power spectrum [23]; SCNN [18] with the short-time Fourier transform (STFT), a fine-tuned CNN model [17] with smooth pseudo-Wigner-Ville distribution and Born-Jordan distribution Achieves high accuracy of PSK [23], recognizes OFDM well, which is revealed only in the spectrum domain due to its plentiful sub-carriers [17] In recent years, several studies have also focused on the advantages of multimodal information fusion for AMR tasks. In [24], modality discriminative features are captured separately using three Resnet networks, and I/Q, A/P, and the amplitudes of spectrum, square spectrum, and fourth power spectrum features are concatenated with the corresponding bitwise summation.…”

Section: Domains Models Effectsmentioning

confidence: 99%

IDAF: Iterative Dual-Scale Attentional Fusion Network for Automatic Modulation Recognition

Liu,

Ge,

Zhu

et al. 2023

Sensors

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Recently, deep learning models have been widely applied to modulation recognition, and they have become a hot topic due to their excellent end-to-end learning capabilities. However, current methods are mostly based on uni-modal inputs, which suffer from incomplete information and local optimization. To complement the advantages of different modalities, we focus on the multi-modal fusion method. Therefore, we introduce an iterative dual-scale attentional fusion (iDAF) method to integrate multimodal data. Firstly, two feature maps with different receptive field sizes are constructed using local and global embedding layers. Secondly, the feature inputs are iterated into the iterative dual-channel attention module (iDCAM), where the two branches capture the details of high-level features and the global weights of each modal channel, respectively. The iDAF not only extracts the recognition characteristics of each of the specific domains, but also complements the strengths of different modalities to obtain a fruitful view. Our iDAF achieves a recognition accuracy of 93.5% at 10 dB and 0.6232 at full signal-to-noise ratio (SNR). The comparative experiments and ablation studies effectively demonstrate the effectiveness and superiority of the iDAF.

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Section: Domains Models Effectsmentioning

confidence: 99%

IDAF: Iterative Dual-Scale Attentional Fusion Network for Automatic Modulation Recognition

Liu,

Ge,

Zhu

et al. 2023

Sensors

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“…Features σ ap and σ dp characterize the variations in a signal's instantaneous phase. σ ap is used to discriminate the order of PSK [4,44], and it distinguishes among ASK2, ASK4, PSK2, and PSK4, Therefore, the gap mainly depends on the absolute phase information to discriminate the modulation formats. σ dp uses the direct phase information to distinguish the modulation types (containing the direct information phase) from those that do not have the direct information phase, such as distinguishing among ASK2, ASK4, and PSK [35,43].…”

Section: Kurtosis Of the Normalized Centered Instantaneous Amplitudementioning

confidence: 99%

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

et al. 2019

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The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.

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“…The sixth feature extraction key used is the mean value of the amplitude designated as X . It is defined mathematically by [31] as:…”

Section: Amr Pattern Recognition Approach First Subsystemmentioning

confidence: 99%

Development and Comparative Study of Effects of Training Algorithms on Performance of Artificial Neural Network Based Analog and Digital Automatic Modulation Recognition

Popoola¹,

Olst²

2015

JESTR

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This paper proposes two new classifiers that automatically recognise twelve combined analog and digital modulated signals without any a priori knowledge of the modulation schemes and the modulation parameters. The classifiers are developed using pattern recognition approach. Feature keys extracted from the instantaneous amplitude, instantaneous phase and the spectrum symmetry of the simulated signals are used as inputs to the artificial neural network employed in developing the classifiers. The two developed classifiers are trained using scaled conjugate gradient (SCG) and conjugate gradient (CONJGRAD) training algorithms. Sample results of the two classifiers show good success recognition performance with an average overall recognition rate above 99.50% at signal-to-noise ratio (SNR) value from 0 dB and above with the two training algorithms employed and an average overall recognition rate slightly above 99.00% and 96.40% respectively at-5 dB SNR value for SCG and CONJGRAD training algorithms. The comparative performance evaluation of the two developed classifiers using the two training algorithms shows that the two training algorithms have different effects on both the response rate and efficiency of the two developed artificial neural networks classifiers. In addition, the result of the performance evaluation carried out on the overall success recognition rates between the two developed classifiers in this study using pattern recognition approach with the two training algorithms and one reported classifier in surveyed literature using decision-theoretic approach shows that the classifiers developed in this study perform favourably with regard to accuracy and performance probability as compared to classifier presented in previous study.

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Modulation recognition of symbol shaped digital signals

Cited by 9 publications

References 4 publications

IDAF: Iterative Dual-Scale Attentional Fusion Network for Automatic Modulation Recognition

IDAF: Iterative Dual-Scale Attentional Fusion Network for Automatic Modulation Recognition

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Development and Comparative Study of Effects of Training Algorithms on Performance of Artificial Neural Network Based Analog and Digital Automatic Modulation Recognition

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