Haberleşme Sistemlerinde Derin Öğrenme

Year 2020, , 1012 - 1025, 15.04.2020

Mete Yıldırım , Radosveta İvanova Sokullu , Saliha Pehlivan

https://doi.org/10.31590/ejosat.679929

Cited By: 2

Abstract

Makine öğreniminde derin öğrenme en başarılı öğrenme yöntemi olmuştur. Derin öğrenme özellikle veri miktarının çok olduğu durumlarda diğer makine öğrenimi yöntemlerine açık ara üstünlük sağlarken, verinin az olduğu durumlarda diğer makine öğrenim yöntemlerine yakın bir sonuç üretebilmektedir. Bu yeni öğrenme yöntemi haberleşme teknolojilerinde kullanılan fiziksel katmanların yeniden tasarlanmasından telsiz ağların modellenmesine kadar birçok yeniliğe katkı sunacak potansiyele sahiptir. Özellikle matematiksel modellemesi zor olan haberleşme sistemlerinde, örneğin 5G ve moleküler haberleşme, kolaylık sağlamaktadır. Bundan dolayı derin öğrenmenin haberleşme sistemlerininde uygulanmasını konu alan birçok araştırma son zamanlarda yapılmaktadır. Buna rağmen haberleşme teknolojileriyle ilgili kurum ve araştırmacıların derin öğrenme yöntemlerine olan uzaklığı bu çalışmaların sayısını ve etkisini sınırlı bırakmıştır. Bu sebeple derin öğrenmenin haberleşme teknolojilerine uygulamasını konu alan çalışmaların toplu olarak incelenmesi, elde edilen başarıların değerlendirilmesi, yapabilecek yeni araştırma konularının belirlenmesine katkı sunacak çalışmalara gerek duyulmaktadır. Bu amaca yönelik olarak bu çalışmada öncelikle derin öğrenme yöntemi, başarıları ve kullanım alanları özetle sunuldu ve haberleşme teknolojilerinin gelişmesine katkı sunan çalışmalar sınıflandırılarak karşılaştırmalı incelendi. Derin öğrenmenin haberleşmede daha başarılı kullanımı için yapılması gerekenler tartışıldı ve yeni nesil haberleşme sistemlerine öncülük edebilecek derin öğrenme tabanlı araştırma alanları belirlendi.

Keywords

Akıllı Haberleşme, Haberleşme, Derin öğrenme, Makine Öğrenimi, Telsiz Haberleşme

Deep Learning for Communication Systems

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.

Keywords

Communication, Deep Learning, Machine Learning, Intelligent Communication

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