Ergodic Capacity Estimation and Performance Analysis with Deep Learning Methods in NOMA-based Cognitive Radio Systems

Mustafa Namdar; Abdulkadir Güney; Fatma Kebire Bardak; Arif Başgümüş

Makale detayları

Ergodic Capacity Estimation and Performance Analysis with Deep Learning Methods in NOMA-based Cognitive Radio Systems

Yazarlar:

Mustafa Namdar | Abdulkadir Güney | Fatma Kebire Bardak | Arif Başgümüş

Yayıncı:

Bursa Uludağ University

Ana Başlıklar:

Elektrik Mühendisliği | İletişim ve Medya Çalışmaları

Dergi:

Uludağ Üniversitesi Mühendislik Fakültesi Dergisi Vol.28 Issue 1, pp.253-272

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001287419

pISSN:

2148-4155

Anahtar kelimeler:

Özet:

 In this study, using the cognitive radio CR-based non-orthogonal multiple access NOMA technique, the total ergodic capacity value of the close user is estimated with high accuracy and fast training times through different training algorithms with the proposed feedforward backpropagation artificial neural network ANN and nonlinear autoregressive network with exogenous inputs NARX model. The data set used in the neural network was obtained from the CR-NOMA system model, which was modeled with the exponential fading channel characteristic. By training the input and output data to the ANN, which was designed using the supervised learning method, ergodic capacity estimates of the close user were made over the test data. While evaluating the performance of ANN and NARX neural networks, the training time, the number of iterations, and the conditions of the network not reaching saturation were taken into consideration. The actual ergodic capacity value of the close user and the predicted values of feedforward backpropagation ANN and NARX networks were compared. The performance analysis of the proposed neural networks under Levenberg-Marquardt, Bayesian and Scaled-Conjugate training algorithms has been examined in terms of epoch value graph, error histogram analysis, and training state analysis where the error reaches the minimum.

 In this study, using the cognitive radio CR-based non-orthogonal multiple access NOMA technique, the total ergodic capacity value of the close user is estimated with high accuracy and fast training times through different training algorithms with the proposed feedforward backpropagation artificial neural network ANN and nonlinear autoregressive network with exogenous inputs NARX model. The data set used in the neural network was obtained from the CR-NOMA system model, which was modeled with the exponential fading channel characteristic. By training the input and output data to the ANN, which was designed using the supervised learning method, ergodic capacity estimates of the close user were made over the test data. While evaluating the performance of ANN and NARX neural networks, the training time, the number of iterations, and the conditions of the network not reaching saturation were taken into consideration. The actual ergodic capacity value of the close user and the predicted values of feedforward backpropagation ANN and NARX networks were compared. The performance analysis of the proposed neural networks under Levenberg-Marquardt, Bayesian and Scaled-Conjugate training algorithms has been examined in terms of epoch value graph, error histogram analysis, and training state analysis where the error reaches the minimum.

 In this study, using the cognitive radio CR-based non-orthogonal multiple access NOMA technique, the total ergodic capacity value of the close user is estimated with high accuracy and fast training times through different training algorithms with the proposed feedforward backpropagation artificial neural network ANN and nonlinear autoregressive network with exogenous inputs NARX model. The data set used in the neural network was obtained from the CR-NOMA system model, which was modeled with the exponential fading channel characteristic. By training the input and output data to the ANN, which was designed using the supervised learning method, ergodic capacity estimates of the close user were made over the test data. While evaluating the performance of ANN and NARX neural networks, the training time, the number of iterations, and the conditions of the network not reaching saturation were taken into consideration. The actual ergodic capacity value of the close user and the predicted values of feedforward backpropagation ANN and NARX networks were compared. The performance analysis of the proposed neural networks under Levenberg-Marquardt, Bayesian and Scaled-Conjugate training algorithms has been examined in terms of epoch value graph, error histogram analysis, and training state analysis where the error reaches the minimum.

 Bu çalışmada, bilişsel radyo BR tabanlı dikgen olmayan çoklu erişim tekniği NOMA kullanılarak, yakın kullanıcıya ait toplam ergodik kapasite değerinin, önerilen ileri beslemeli geri yayılımlı yapay sinir ağı YSA ve doğrusal olmayan dışsal girdili otoregresif ağ Nonlinear Autoregressive Network with Exogenous Inputs, NARX modeli ile farklı eğitim algoritmaları yoluyla yüksek doğruluk oranında ve hızlı eğitim sürelerinde tahmin edilmesi amaçlanmıştır. Sinir ağında kullanılan veri seti, üstel sönümleme kanalı karakteristiği ile modellenen BR-NOMA sistem modelinden elde edilmiştir. Denetimli öğrenme yöntemi kullanılarak tasarlanan YSAya girdi ve çıktı verileri öğretilerek yakın kullanıcıya ait ergodik kapasite tahmini yapılmıştır. YSA ve NARX sinir ağları başarımı değerlendirilirken eğitim süresi, iterasyon sayısı, ağın doygunluğa ulaşmaması durumları göz önünde bulundurulmuştur. Yakın kullanıcıya ait gerçek ergodik kapasite değeri ile ileri beslemeli geri yayılımlı YSA ve NARX ağlarının tahmin etmiş olduğu değerler karşılaştırılmıştır. Önerilen sinir ağlarının Levenberg-Marquardt, Bayesian ve Scaled-Conjugate eğitim algoritmaları altındaki performans analizi, hatanın minimuma ulaştığı epok değer grafiği, hata histogram analizi ve eğitim durum analizi açılarından incelenmiştir.

Makale detayları

Ergodic Capacity Estimation and Performance Analysis with Deep Learning Methods in NOMA-based Cognitive Radio Systems

Yazarlar:

Mustafa Namdar | Abdulkadir Güney | Fatma Kebire Bardak | Arif Başgümüş

Yayıncı:

Bursa Uludağ University

Ana Başlıklar:

Elektrik Mühendisliği | İletişim ve Medya Çalışmaları

Dergi:

Uludağ Üniversitesi Mühendislik Fakültesi Dergisi Vol.28 Issue 1, pp.253-272

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001287419

pISSN:

2148-4155

Bayesian

Cognitive radio

Levenberg-Marquardt

NARX

NOMA

Artificial neural networks

Bayesian

Bilişsel radyo

Levenberg-Marquardt

NARX

NOMA

Yapay sinir ağları

Sayı

Makale detayları

Yazarlar: Mustafa Namdar | Abdulkadir Güney | Fatma Kebire Bardak | Arif Başgümüş

Yayıncı: Bursa Uludağ University

Ana Başlıklar: Elektrik Mühendisliği | İletişim ve Medya Çalışmaları

Dergi: Uludağ Üniversitesi Mühendislik Fakültesi Dergisi Vol.28 Issue 1, pp.253-272

Belge Tipi: Makale

Yayın Tarihi: 4/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001287419

pISSN: 2148-4155

Sayı

Yazarlar:

Mustafa Namdar | Abdulkadir Güney | Fatma Kebire Bardak | Arif Başgümüş

Yayıncı:

Bursa Uludağ University

Ana Başlıklar:

Elektrik Mühendisliği | İletişim ve Medya Çalışmaları

Dergi:

Uludağ Üniversitesi Mühendislik Fakültesi Dergisi Vol.28 Issue 1, pp.253-272

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001287419

pISSN:

2148-4155