Haberleşme Sistemlerinde Derin Öğrenme

Abstract

Deep learning has become the most successful learning method in machine learning. While deep learning provides a clear advantage over other machine learning methods, especially when the amount of data is high, it can produce an approximate result to other machine teaching methods when data is low. This new learning method has the potential to contribute to many innovations, from redesigning the physical layers used in communication technologies to modeling wireless networks. It is particularly useful in communication systems where mathematical modeling is difficult, for example, 5G and molecular communication. Therefore, many types of research on the application of deep learning in communication systems have been conducted recently. However, the distance of institutions and researchers about communication technologies to deep learning methods has limited the number and impact of these studies. Therefore, it is necessary to collectively examine the studies that involve the application of deep learning to communication technologies, to evaluate the achievements, and to contribute to the determination of new research topics. For this purpose, in this study, firstly, the achievements of deep learning and usage areas are summarized and then the studies that contribute to the development of communication technologies are classified and examined comparatively. To make deep learning more effective in communication, what needs to be done were discussed and deep learning based research areas that could lead to next generation communication systems were determined.