Automatic Modulation recognition using Genetic Programming and Multi-Layer Perceptron Neural Network
Author(s):
Abstract:
This paper shows how we can make advantage of using genetic programming in selection of suitable features for automatic modulation recognition. Automatic modulation recognition is one of the essential components of modern receivers. In this regard, selection of suitable features may significantly affect the performance of the process. In this research we implemented our model by using appropriate software and hardware platforms. Simulations were conducted with 5db and 10db SNRs. We generated test and training data from real ones recorded in an actual communication system. For performance analysis of the proposed method a set of experiments were conducted considering signals with 2ASK, 4ASK, 2PSK, 4PSK, 2FSK and 4FSK modulations. The results show that the selected features by the suggested model improve the performance of automatic modulation recognition considerably. During our experiments we also reached the optimum values and forms for mutation and crossover ratio, elitism policy, fitness function as well as other parameters for the proposed model.
Keywords:
Language:
Persian
Published:
Signal and Data Processing, Volume:11 Issue: 1, 2014
Page:
49
https://www.magiran.com/p1311119
سامانه نویسندگان
مقالات دیگری از این نویسنده (گان)
-
A New Structure for Perceptron in Categorical Data Classification
Fariba Taghinezhad, *
Journal of Artificial Intelligence and Data Mining, Summer 2024 -
Improvement of generative adversarial networks for automatic text-to-image generation
Elham Pejhan, *
Signal and Data Processing,