Automatic Modulation Recognition of Mixed Multiple Source Signals
-
Xiaobo Tan
,
Hang Zhang
Abstract
In this paper, automatic modulation recognition of mixed multiple source signals is discussed. At first, the algorithm of equivariant adaptive source separation (EASI) is employed to separate signals from their mixed waveforms. Four features of five modulated signals are extracted and then two classifiers, decision tree and neutral network are used to complete modulation classification. The effects of symbol shaping on features extraction and validation of source separation are also investigated. Simulations show that the average probability of correct recognition of the classifiers is very depended on the performance of source separation. When SNR (Signal to Noise Ratio) is larger than 15 dB and the number of mixed source signals is less than 4, the average probability of correct recognition is above 0.6 for decision tree classifier and 0.63 for neutral network classifier. Simulations and discussions about automatic modulation recognition for source signals surfed Rayleigh flat fading are also presented.
Copyright © 2011 De Gruyter
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Articles in the same Issue
- Editorial
- Design of Compact Filter Based on Multilayered Left-Handed Transmission Line Using LTCC Technology
- A Metamaterial Inspired Compact Patch Antenna with Frequency Continuously Reconfigurable Performance for Cognitive Radio
- Nematic LCs Mixtures with High Birefringence in the Microwave Region
- A Fast-Locking Frequency Synthesizer for the Single-Chip CMOS UHF RFID Reader
- A Compact Analytical Design of Dual-Loop 18 GHz Frequency Synthesizer to Enhance Signal Reliability in Digital Millimeter Radio Link System
- Automatic Modulation Recognition of Mixed Multiple Source Signals
- A Current-Mode Common-Mode Feedback Circuit (CMFB) with Rail-to-Rail Operation
- Diversity and Multiplexing Tradeoff Analysis of Fractional Cooperative Networks
