Table 2 ANN within cognitive radio
From: Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network
Reference | Brief summary |
|---|---|
[21] | Wavelet cyclic feature has been proposed to reduce the complexity of calculating classical cyclic spectrum and a FFNNa has been used to classify the received signals into BPSKb, QPSKc, MSK,d and 2-FSKe. Cons: limited to low-order modulation schemes. |
[22] | Based on the instantaneous temporal features (the maximum value of the spectral power density, the standard deviation of the direct and absolute instantaneous phase values and the standard deviation of the normalized instantaneous amplitude), the authors have proposed a FFNN and probabilistic ANN to classify the received signals into 2- and 4-ASKf, BPSK, QPSK, 2-, and 4-FSK, 8-PSKg, 16-QAMh. Cons: Large ANN architecture and requires prior information on some specific parameters to guarantee the highest accuracy and reliable recognition. |
[23] | Based on the instantaneous temporal features (the maximum value of the spectral power density, the standard deviation of the direct and absolute instantaneous phase and the standard deviation of the normalized instantaneous amplitude), the authors have proposed a simple FFNN to classify the received signals into five classes, namely; 2- and 4-ASK, BPSK, and QPSK. Cons: low-order modulations were considered. This approach requires prior information on some specific parameters to guarantee the highest accuracy and reliable recognition. |
[25] | Based on the extracted CWT instantaneous features (the mean, variance and central moments values), the authors have proposed an ANN to classify the modulation scheme into k-ASK, k-PSK, k-FSK, k-QAM, OOK,i and MSK. |
[26] | Based on the extracted instantaneous temporal features, the authors proposed a rule-based approach to discriminate between 15 modulation schemes (AMj, FMk, DSBl, LSBm, USBn, VSBo, combined AM–FM, CW, Noise, 2-, and 4-ASK, 2- and 4-PSK, 2-, and 4-FSK). Cons: due to limited number of features and signal sensitivity, the approach was unable to classify the same modulation schemes of different order. |
[37] | FFNN, radial basis function ANN and multi-class support vector machine (SVM) have been suggested to classify the modulation technique of the received signal into 2- and 4-FSK, 4-ASK, 8-ASK, 2-PSK, 4-PSK, 8-PSK, V32, 8-, 16-, 32-, and 64-QAM. Cons: it requires prior information on specific parameters to guarantee the highest accuracy and reliable recognition. |
[38] | An expert discrete wavelet adaptive network based on fuzzy inference system has been proposed for classifying the digital modulated signals into 8-ASK, 8-FSK, 8-PSK, and 8-QAM. Cons: very large ANN structure, with four hidden layers. |
[39] | A system that is only based on wavelet transform has been developed, where a comparison between signals and templates in wavelet domain has been adapted to classify the received signals into 2-ASK, 2-FSK, and BPSK. Cons: binary digital modulation schemes were considered. It also requires prior signal information, such as, carrier frequency and symbol duration. |
[40] | A system that is based solely on DWT and signals statistics was used to classify the modulated received signals into 16-QAM, QPSK and BPSK. Cons: degradation of performance at SNR appeared when the ANN was trained on signals with lower SNR. |