Modulation Recognition Using Artificial Neural Networks
In this chapter the artificial neural networks (ANNs) approach as another solution for the modulation recognition process is studied in some detail. Unlike in other algorithms, especially those which utilise the decision-theoretic (DT) approach (Chapters 2–4), where a suitable threshold for each key feature has to be chosen, the threshold at each node (neuron) is chosen automatically and adaptively. Furthermore, in the DT approach, many algorithms based on the same key features can be developed by applying the extracted key features in different order in the classification algorithm and they perform with different success rates at the same SNR. In the DT algorithms, it was found that only one key feature is considered at a time. As a result, the probability of correct decision about a modulation type in these algorithms is based on the time-ordering of the key features used as well as probability of correct decision derived from each key feature. On the other hand, in the ANN algorithms all the key features are considered simultaneously. So, the time order of the key features does not affect on the probability of correct decision of on the modulation type of a signal. For that reason, it is suggested that the use of the ANN approach for solving the modulation recognition process may have better performance than the DT approach.
KeywordsHide Layer Output Layer Modulation Type Correct Decision Single Hide Layer
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