Replication Data for: Multi-Dimensional Wireless Signal Identification Based on Support Vector Machines
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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 togethe' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Cyclostationarity","Deep Learning","Machine Learning","Signal Classification","Support Vector Machine"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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 togethe' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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 togethe
Cyclostationarity; Deep Learning; Machine Learning; Signal Classification; Support Vector Machine