A New Generation Communication System Based on Deep Learning Methods for the Process of Modulation and Demodulation from the Modulated Images

Demodulating the modulated signals used in digital communication on the receiver side is necessary in terms of communication. The currently used systems are systems with a variety of hardware. These systems are used separately for each type of communication signal. A single algorithm facilitates the classification and subsequent demodulation of signals without needing hardware instead of extra hardware cost and complex systems. This study, which aims to make modulation classification by using images of signals, provides this convenience. In this study, a classification and demodulation process is done by using images of digital modulation signals. Convolutional neural network (CNN), a deep learning algorithm, has been used for classification and recognition. Images of the signals of quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase shift keying (QPSK) digital modulation types at noise levels of 0 dB, 5 dB, 10 dB, and 15 dB were used. Thanks to this algorithm, which works without hardware, the success achieved is around 98%. Python programming language and libraries have been used in training and testing the algorithm. Demodulation processes of these signals have been performed for demodulation using the nonlinear autoregressive network with exogenous inputs (NARX) algorithm, an artificial neural network. As a result of using MATLAB, the NARX algorithm achieved approximately 94% success in obtaining the information signal. Thanks to the work done, it will be possible to classify and demodulate other communication signals without extra hardware.