Çöz Aktar İşbirlikli Çeşitlemeli Çok Röleli Sistemlerde Derin Öğrenme Yardımlı En İyi Röle Seçimi Ve Güç Optimizasyonu

İşbirlikli Haberleşme sistemi kaynak, röleler ve hedef düğümlerden oluşmaktadır. Kaynak, işaretleri rölelere ve hedefe, röleler ise çözdüğü işareti hedef düğüme aktarmaktadır. İşbirlikli haberleşme sistemlerinde spektral verimliliğin korunması açısından en iyi röle seçimi önemli bir husustur. Ayrıca güvenli bir iletişim için kaynak röle ve röle hedef düğümleri arasındaki işaret gürültü oranlarının da maksimum yapılması gereklidir. Derin öğrenme (deep learning-DL) tekniği fiziksel seviye haberleşme tekniklerinde yaygın olarak kullanılmaya başlanan bir tekniktir. DL var olan haberleşme tekniklerine alternatif çözümler sunmaktadır. Bu çalışmada DL tekniği ile en iyi röle seçilmiştir. En iyi röle seçimi, kaynaktan rölelere ve röleler hedef arasında iletim yapılırken güç optimizasyonu da göz önünde bulundurularak yapılmıştır. Evrişimli sinir ağı (Convolutional Neural Network-CNN) tekniği ile bulunan sonuçlar geleneksel maxmin yöntemi ile tespit edilen en iyi röle ile bulunan sonuçlardan hata performansı açısından başarılıdır. Ayrıca DL ile hata başarımlarına güç optimizasyonu da önemli bir etki sağlamaktadır.

Anahtar Kelimeler:

CNN, BPSK, DF, İşbirlikli Haberleşme, Derin Öğrenme

Best Relay Selection And Power Optimization In Deep Learning Aided Multi-Relay System With Decode And Forward Cooperative Diversity

Cooperative Communication system consists of source, relays and destination nodes Source transmits signals to relays and target node, and relays transmit the decoded signals to the target node. The best relay selection is an important issue in terms of maintaining spectral efficiency in cooperative communication systems. In addition, for a secure communication, it is necessary to maximize the signal-to-noise ratios between the source relay and relay destination nodes. Deep learning (DL) technique has been a technique that have been widely used among physical level communication techniques. DL proposes alternative solutions to existing communication techniques. In this study, the best relay has been selected with DL technique. Best relay selection has been implemented by considering power optimization while transmitting from source to the best relay and transmitting between the best relay and target node. The results obtained with the Convolutional Neural Network (CNN) technique are more successful in terms of error performance than the results found with the best relay detected by the traditional maxmin method. In addition, power optimization also has a significant effect on error performance with DL.

Keywords:

CNN, BPSK, DF, Cooperative Communication, Deep Learning,

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