Abstract
Automatic Digital Modulation Recognition (ADMR) is becoming an interesting problem with various civil and military applications. In this paper, an ADMR algorithm in Multi-Carrier Code Division Multiple Access (MC-CDMA) systems using Discrete Transforms (DTs) and Mel-Frequency Cepstral Coefficients (MFCCs) is proposed. This algorithm uses various DT techniques such as the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) with MFCCs to extract features from the modulated signal and a Support Vector Machine (SVM) to classify the modulation orders. The proposed algorithm avoids over fitting and local optimal problems that appear in Artificial Neural Networks (ANNs). Simulation results shows the classifier is capable of recognizing the modulation scheme with high accuracy up to 90% - 100% using DWT, DCT and DST for some modulation schemes over a wide Signal-to-Noise Ratio (SNR) range in the presence of Additive White Gaussian Noise (AWGN) and Rayleigh fading channel, particularly at a low Signal-to-Noise ratios (SNRs).
Highlights
Automatic Digital Modulation Recognition (ADMR) is an intermediate step between signal detection and demodulation
ADMR techniques usually can be categorized in two main principles, the first is based on the Decision-Theoretic Approach (DTA) and the second is based on the Pattern Recognition Approach (PRA).The DTA is a probabilistic solution based on a priori knowledge of probability functions and certain hypotheses [2], and the PRA consists of two subsystems
Once a proper set of feature vectors is obtained from the previous stage, the Support Vector Machine (SVM) develop a model for each modulated signal features for building the reference models into the system database. the modulated signals are fed to the spreading process, the Inverse Fast Fourier Transform (IFFT) is applied for the multiplexing processes to distinguish between the subchannels and to generate Multi-Carrier Code Division Multiple Access (MC-CDMA) signal
Summary
ADMR is an intermediate step between signal detection and demodulation. It is a rapidly evolving area in various digital signaling systems currently developed or planned for various civilian and military communication applications [1]. In [5], the authors presented a kernel-based modulated signal classification method for a Cognitive Radio (CR) system, which cast into machine learning approaches This method extracts both statistical and spectral features from the received signals. Simulation results showed that this identifier is able to identify different types of modulations (e.g. QAM64, V.29, and ASK8) with high accuracy even at low SNRs. In [9], an approach to signal classification combining spectral correlation analysis and SVMs was introduced. We propose an algorithm capable of identifying digital modulation techniques in MC-CDMA system, and after identification it is capable of recovering transmitted data This algorithm uses different discrete transform techniques (DWT, DCT and DST) for features extraction, and applies MFCCs to obtain the useful features.
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