{'fraction_non_alphanumeric': 0.013285024154589372, 'fraction_numerical': 0.0024154589371980675, 'mean_word_length': 5.28030303030303, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Modeling and simulation of an recognition system for digital modulated signals

Automatic modulation type identification is needed in many applications. The recognition of the communication system for example is done by inspecting the received signal properties like modulation type, carrier frequency, baud rate and so on. In this paper we introduce a new AMR (Automatic Modulation Recognition) method based on time and spectral domain features of the received signal. A broad class of analog and digital modulations is considered. Baud rate and carrier frequency estimation is performed by existing methods. The proposed model was tested through simulations in LabVIEW software. Using a noisy input signal revealed that the signal to noise lower limit for the analogue and digital modulation classifier is approximately 10 dB.