Spectrum Sensing and Signal Identification with Deep Learning based on Spectral Correlation Function

Spectrum sensing is one of the means of utilizing the scarce source of wireless spectrum efficiently. In this paper, a convolutional neural network (CNN) model employing spectral correlation function which is an effective characterization of cyclostationarity property, is proposed for wireless spectrum sensing and signal identification. The proposed method classifies wireless signals without a priori information and it is implemented in two different settings entitled CASE1 and CASE2. In CASE1, signals are jointly sensed and classified. In CASE2, sensing and classification are conducted in a sequential manner. In contrary to the classical spectrum sensing techniques, the proposed CNN method does not require a statistical decision process and does not need to know the distinct features of signals beforehand. Implementation of the method on the measured overthe-air real-world signals in cellular bands indicates important performance gains when compared to the signal classifying deep learning networks available in the literature and against classical sensing methods. Even though the implementation herein is over cellular signals, the proposed approach can be extended to the detection and classification of any signal that exhibits cyclostationary features. Finally, the measurement-based dataset which is utilized to validate the method is shared for the purposes of reproduction of the results and further research and development

Multi–Dimensional Wireless Signal Identification Based on Support Vector Machines

ABSTRACT: Radio air interface identification provides necessary information for dynamically and efficiently exploiting the wireless radio frequency spectrum. In this study, a general machine learning framework is proposed for Global System for Mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), and Long Term Evolution (LTE) signal identification by utilizing the outputs of the spectral correlation function (SCF), fast Fourier Transform (FFT), auto-correlation function (ACF), and power spectral density (PSD) as the training inputs for the support vector machines (SVMs). In order to show the robustness and practicality of the proposed method, the performance of the classifier is investigated with respect to different fading channels by using simulation data. Various over-the-air real-world measurements are taken to show that wireless signals can be successfully distinguished from each other without any prior information while accounting for a comprehensive set of parameters such as different kernel types, number of in-phase/quadrature (I/Q) samples, training set size, or signal-to-noise ratio (SNR) values. Furthermore, the performance of the proposed classifier is compared to the existing well-known deep learning (DL) networks. The comparative performance of the proposed method is also quantified by classification confusion matrices and Precision/Recall/F-1-scores. It is shown that the investigated system can be also utilized for spectrum sensing and its performance is also compared with that of cyclostationary feature detection spectrum sensing

Replication Data for: Multi-Dimensional Wireless Signal Identification Based on Support Vector Machines

The dataset includes spectral correlation function (SCF) estimations by FFT accumulation method (FAM) for totally 4500 signals with 20000 I/Q samples (but only 16384 samples are used). The signals belong to three different cellular communication standards: GSM, WCDMA, and LTE. The signals have been received from different channels with multipath, fading, and noise. Furthermore, the dataset provides other features such as Fast Fourier Transform (FFT), Autocorrelation (ACF), and Power Spectral Density (PSD) in linear scale. The dataset can be used to validate the designed classifier model aiming to identify cellular communication signals. For each signal, the dimension of SCF estimate (alpha domain profile maximizing over spectral frequency) is 1*32769. There are four train sets which must be used together (SCF_train1.mat, SCF_train2.mat, SCF_train3.mat, and SCF_train4.mat). Four train sets for each feature have 3000 signals totally, and two test sets for each feature have 1500. The label of the cellular communication standards are given in dataset as follows: WCDMA -> 0 LTE -> 1 GSM -> 2 The compressed file includes: 1. ACF Folder 2. FFT Folder 3. PSD Folder 4. SCF Folder Each folder above consists of two folder: Test and Train. The test set is located in the Test folder as two parts and the train set is located in the Train folder as four parts. The contents of .mat files: training_class_k : denotes class labels corresponding to the training_data_k, its dimension is 750*1 double training_data_k : includes the kth quarter of the training data, its dimension is 750*32769 double test_class_k : denotes class labels corresponding to the test_data_k, its dimension is 750*1 double test_data_k : includes the kth half of the test data, its dimension is 750*32769 double The dataset has been used for the paper "Multi-Dimensional Wireless Signal Identification Based on Support Vector Machines" submitted for possible publication in IEEE Access. Please cite this paper, if you use the dataset.</p

Replication Data for:"Real-World Considerations for Deep Learning in Wireless Signal Identification Based on Spectral Correlation Function"

The dataset includes spectral correlation function (SCF) estimations by FFT accumulation method (FAM) for totally 4500 signals with 20000 I/Q samples. The signals belong to three different cellular communication standards: GSM, WCDMA, and LTE. The signals have been received from the different channels with multipath, fading, and noise. The dataset can be used to validate the designed classifier model aiming to identify cellular communication signals. For each signal, the dimension of SCF estimate is 8193*16. There are two train sets which must be used together (concatenate train_data_wo_mapping1 and train_data_wo_mapping2 ). Two train sets have 3000 signals totally, and the test set has 1500. The label of the cellular communication standards are given in dataset as follows: WCDMA -> 0 LTE -> 1 GSM -> 2 The dataset includes: 1. SCFDatatrain1.mat 2. SCFDatatrain2.mat 3. SCFDatatest.mat The contents of .mat files: train_class : denotes class labels of the train set, its dimension is 3000*1 double train_data_wo_mapping1 : includes the first half of the training data, its dimension 1500*1 cell train_data_wo_mapping2 : includes the second half of the training data, its dimension 1500*1 cell *Note, concatenate two cells given above (ie [train_data_wo_mapping1; train_data_wo_mapping2]) test_class : denotes class labels of the train set, its dimension is 1500*1 double test_data_without_mapping : includes the test data, its dimension 1500*1 cell Each cell contains 1500 SCF estimates (8193*16) . The dataset has been used for the paper "Real-World Considerations for Deep Learning in Wireless Signal Identification Based on Spectral Correlation Function" submitted for possible publication in IEEE Wireless Communication Letters. Please cite this paper, if you use the dataset