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| An Automatic Modulation Recognizer Using Neural Networks Based on the Hybrid Mode |
| Lai Hui-cheng; Chu Hui |
| College of Information Science & Engineering, Xinjiang University, Urumqi 830046, China |
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Abstract On automatic modulation there are two approaches, decision-theoretic and statistical pattem. An automatic modulation recognition system to recognize four digital signal classes as MASK, MFSK, MPSK, MQAM is proposed in this paper, which using decision-theoretic based feature set addition to statistical pattem based feature set with momentum auto-adapted weight BP neural network. Performance is generally good when Signal to Noise Ratios (SNR) in 0-10dB, and the estimated carrier frequency differs from the actual carrier frequency of 0-100Hz, simulations show the results even larger than 97%, that confirm the robustness and practicability of this recognition method.
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Received: 06 April 2007
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2008, Vol. 30 
