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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
The numerical results demonstrate the error performance degradation due to fading channel frame numbers and the state of the encoder.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, the performance of turbo coded signals are investigated for different frame numbers -and encoder structures over Rician fading channels.
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Key Findings & Data:In this paper, the performance of turbo coded signals are investigated for different frame numbers -and encoder structures over Rician fading channels.
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Conclusion & Impact:The numerical results demonstrate the error performance degradation due to fading channel frame numbers and the state of the encoder.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
In this paper, the performance of turbo coded signals are investigated for different frame numbers -and encoder structures over Rician fading channels. The numerical results demonstrate the error performance degradation due to fading channel frame numbers and the state of the encoder.
Özet
Bu makale de, farklı çerçeve uzunlukları, kodlayıcı özelliklerine göre değiştirilerek turbo kodlanmış işaretlerin Rician ortamlarda başarımı incelenmiştir. Benzetim sonuçları, söz konusu Rician ortamda farklı parametreler için değerlendirilmiştir.
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Cite This Article
OSMAN, O., BAYAT, O., ALNAJJAR, H., UÇAN, O. N. (2022). Turbo kodlanmış işaretlerin sönümlemeli kanallarda başarım analizi. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 2 - Sayı: 1, 417–422. https://ajindex.com/articles/543943