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제안 방법
- In this paper, the two minimum distance classifiers and two k-nearest neighbor classifiers to simultaneously recognize different analog and digital modulated signals have been presented to investigate for real-time software radio application.
- In this paper, we investigate the performances of the two types of fast modulation classifiers (i.e., 2 minimum distance classifiers and 2 k-nearest neighbor classifiers) with 7 features for 9 types of modulation signals and compare the performance of these classifiers with that of decision theoretic classifier (MDC for short) and support vector machine classifier (SVC for short). The probability of correct classification (Pcc) and the classification time of these classifiers are compared through numerical simulations.
- In this section, the numerical simulations are performed for all type of modulation signals of interest in order to evaluate the Pcc and processing time of the classifiers. It has known that none of many classification approaches have been proven to work reliably with signals that have low SNR (below 10dB), or when a large range of modulation types including both digital and analog is being considered.
- The aim of this paper is to recognize simultaneously analog and digital modulation types, although there is a tendency to move towards using digital modulation schemes, there are currently still analog methods (i.e., legacy radios) in use. It is also possible to extract simply the message from an analog signal once it has been demodulated, which is more useful than the coded message which is received from a demodulated digital signal.
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