Year 2021, Volume 8 , Issue 1, Pages 90 - 99 2021-06-30

LSTM tabanlı Derin Sinir Ağı ile Beşinci Nesil Küçük Hücre Ağlarında El Değiştirme Tahmini
Handover Prediction in Fifth Generation Small Cell Networks with LSTM-based Deep Neural Network

Murtaza CİCİOĞLU [1]


Bu çalışmada, Uzun Kısa-Vadeli Hafıza (LSTM) tabanlı derin sinir ağı ile beşinci nesil küçük hücre ağlarında el değiştirme (handover, HO) tahminlerini gerçekleştiren yeni bir model geliştirilmiştir. İlk olarak HO tahmininde eğitim için kullanılacak olan veri seti Riverbed Modeler benzetim yazılımında tasarlanan benzetim senaryoları ile oluşturulmuştur. Bu senaryolar aracılığıyla sinir ağının veri kümesinde kullanılacak üç adet giriş (RSSI, SNR ve Jitter) değişkeni ve bir adet çıkış (istenen değer) değişkeni elde edilmiştir. Bu veri seti makine öğrenmesi algoritmalarından LSTM, SVM, Tree ve Lineer Regresyon teknikleri ile eğitilmiştir. LSTM tabanlı derin sinir ağı diğer regresyon algoritmaları ile karşılaştırılmış ve daha yüksek başarıma sahip olduğu tespit edilmiştir. LSTM için eğitilen modelin test sonuçları incelendiğinde; R2 0.94, MAE 0.3315, MSE 0.3670 ve RMSE değeri 0.6058 olarak bulunmuştur. LSTM tabanlı derin sinir ağlarının, regresyon işlemlerinde yüksek başarım gösterdiği görülmüştür. Sonuç olarak önerilen regresyon modeli ile 5G küçük hücre ağlarında HO kararlarının tahmin edilebildiği gösterilmiştir.

In this study, a new model is developed that performs handover prediction in fifth generation small cell networks with Long Short Term Memory (LSTM) based deep neural networks. Firstly, the data set to be used for training in handover prediction was created with simulation scenarios designed in Riverbed Modeler. Through these scenarios, three input (RSSI, SNR and Jitter) variables and one output variable (desired value) were obtained to be used in the data set of the neural network. This data set was trained with machine learning algorithms LSTM, SVM, Tree, and Linear Regression techniques. LSTM-based deep neural network was compared with other regression algorithms and was found to have higher performance. When the test results of the trained model for LSTM are examined; R2 0.94, MAE 0.3315, MSE 0.3670, and RMSE value 0.6058 was found. It was observed that LSTM-based deep neural networks show high performance in regression processes. As a result, study shows that handover decisions can be predicted in 5G small cell networks with the proposed regression model.

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Primary Language tr
Subjects Engineering
Journal Section Articles
Authors

Orcid: 0000-0002-5657-7402
Author: Murtaza CİCİOĞLU (Primary Author)
Institution: Bolu Milli Eğitim Müdürlüğü
Country: Turkey


Dates

Application Date : December 15, 2020
Acceptance Date : January 8, 2021
Publication Date : June 30, 2021

APA Cicioğlu, M . (2021). LSTM tabanlı Derin Sinir Ağı ile Beşinci Nesil Küçük Hücre Ağlarında El Değiştirme Tahmini . Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi , 8 (1) , 90-99 . DOI: 10.35193/bseufbd.840927