KABLOSUZ İLETİŞİM SİSTEMLERİ İÇİN MAKİNA ÖĞRENİMİ DESTEKLİ ALTERNATİF SEZİCİ TASARIMI

Son yıllarda derin öğrenme (deep learning-DL) teknikleri fiziksel seviye iletişim sistemlerinde yaygın olarak kullanılmaktadır. DL teknikleri, modern haberleşme sistemlerindeki işlem karmaşıklığını azaltmasından ve daha iyi başarım sağlaması gibi nedenlerden dolayı hali hazırda var olan haberleşme yöntemlerine alternatif seçenekler sunmaktadır. Bu çalışmada, Rayleigh sönümlemeli kanalda ikili faz kaydırmalı anahtarlama (binary phase shift keying-BPSK) veya dördün faz kaydırmalı anahtarlama (quadrature phase shift keying-QPSK) modülasyonu kullanılması durumunda alıcıya ulaşan işaretin işaret yıldız kümesi görüntüsünden, gönderilen işaretin DL ile kestirimi hedeflenmiştir. DL tekniklerinden olan evrişimli sinir ağı (convolutional neural network -CNN) girişine alıcıya gelen işaretin ve denkleştirilmiş işaretin işaret yıldız kümesi görüntüsü uygulanmıştır. CNN sınıflandırıcı ile bulunan sistemin hata başarımları klasik en büyük olabilirlikli sezici (maximum likehood-ML) başarımları ile karşılaştırılmıştır. İşaret yıldız kümesinde farklı boyutlarda bölgeler seçilmiştir. Bu bölgelerin her biri ayrı senaryo olarak değerlendirilir. Belirli senaryolar altında bu bölgelerin CNN sınıflandırıcı ile elde edilen hata başarımları ile ML hata başarımları ile benzer çıktığı görülmüştür.

Anahtar Kelimeler:

CNN, QPSK, BPSK, ML, Derin Öğrenme

MACHINE-LEARNING AIDED ALTERNATIVE DETECTOR DESIGN FOR WIRELESS COMMUNICATIONS SYSTEMS

In recent years, deep learning (DL) techniques are widely used for physical layer solutions in communication systems. DL techniques offer alternative options to existing communication methods since they can reduce the computational complexity and provide better performance in modern communication systems. In this study, we propose a DL-aided signal detections for BPSK (Binary Phase Shift Keying) or QPSK (Quadrature Phase Shift Keying) modulation over Rayleigh fading channel where the DL-aided detection is performed based on the constellation diagram image of the received signal. The constellation diagram image of received signal and the equalized signal are given as inputs to the convolutional neural network (CNN), which is one of the commonly used DL techniques, Regions of different sizes are selected in the constellation diagram. Each of these regions is considered as different scenarios. The error performance of the system obtained with the CNN classifier is compared with the classical maximum likelihood (ML) detector performance. Under certain scenarios, it has been revealed that the DL-aided signal detection could achieve the performance of the ML detector which shows the effectiveness of the proposed solution.

Keywords:

CNN, QPSK, BPSK, ML, Machine Learning,

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