UFMC Sistemlerinde Sembol Tespiti İçin Derin Öğrenme Modelleri

Öz

Kablosuz haberleşme sistemlerinde frekans bandının sınırlı kullanılabilirliği, yüksek hızlı veri iletiminin önündeki en büyük engellerden biridir. Bu engeli aşmak için, spektral verimliliği ve dolayısıyla yüksek veri hızı iletimini sağlamak için mevcut frekans bant genişliğini en verimli şekilde kullanan çoklu taşıyıcı sistemler kullanılmaktadır. Çoklu taşıyıcı sistemlerden biri olan Evrensel Filtreli Çoklu Taşıyıcı (UFMC) tekniğinde, yüksek hızlı veri iletiminin yanı sıra, bant genişliği birçok alt banda bölünerek yalnızca alt yan bantlar filtrelenmekte ve bunun sonucunda kanallar arası girişim sorunu en aza indirilmektedir. Ancak UFMC sistemlerde, alıcıda sembollerin hatasız alınması doğrudan sembol tespit algoritmasının performansına bağlıdır. Bu çalışmada, derin öğrenme yöntemlerinin öğrenme yeteneğinden yararlanılarak, doğrusal olmayan problemlerin çözümünde esnek çözümler sunulması, daha az parametre kullanılarak donanım yükünün azaltılması ve paralel işlem yapılabilmesi sayesinde UFMC sistemlerde sembol tespiti gerçekleştirilmiş ve böylece sistemin kötü kanal koşulları altında sembol tespit performansı artırılmıştır.

Anahtar Kelimeler

• UFMC
• Sembol tespiti
• LSTM
• CNN

Deep Learning Models For Symbol Detection in UFMC Systems

Öz

The limited availability of the frequency band in wireless communication systems is one of the major obstacles to achieving high-speed data transmission. To overcome this obstacle, multicarrier systems, which utilize the available frequency bandwidth most efficiently to ensure spectral efficiency and consequently high data rate transmission, are used. In the Universal Filtered Multi-Carrier (UFMC) technique, which is one of the multi-carrier systems, in addition to high-speed data transmission, the bandwidth is divided into many sub-bands and only the lower sidebands are filtered, and as a result, the inter-channel interference problem is minimized. However, in UFMC systems, the error-free reception of symbols at the receiver is directly dependent on the performance of the symbol detection algorithm. In this study, symbol detection was performed in UFMC systems by taking advantage of the learning ability of deep learning methods, providing flexible solutions in solving nonlinear problems, reducing the hardware load by using fewer parameters and the ability to perform parallel processing, and thus the symbol detection performance of the system under bad channel conditions was increased.

Anahtar Kelimeler

• UFMC
• Symbol detection
• LSTM
• CNN

Kaynakça

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