Evre Uyumlu Optik OFDM Sistemler için Karmaşık Aşırı Öğrenme Makinası Tabanlı Doğrusal Olmayan Denkleştirici

Abstract

Radio-over-Fiber (RoF), 5G ve ötesi sistemlerde daha uzun iletişim mesafelerinde daha hızlı veri iletimi için alternatif bir çözüm olarak sunulmaktadır. RoF’un alt bileşenlerinden birisi olan optik haberleşme sistemlerinde evre uyumlu alıcıların kullanımı önemli kazanımlar sağlamaktadır. Bu çalışmada MQAM evre uyumlu-OFDM alıcılar için iletişim kanalının doğrusal ve doğrusal olmayan etkilerini kompanze etmek amacıyla karmaşık-Aşırı Öğrenme Makinesi (K-AÖM) tabanlı doğrusal olmayan denkleştirici önerilmiş ve Monte Carlo benzetimleri ile performans analizleri yapılmıştır.

Keywords

• Karmaşık Aşırı Öğrenme Makinası
• evre uyumlu optik OFDM
• fiber doğrusal olmayan etki.

Complex Extreme Learning Machine-based Nonlinear Equalizer for Coherent Optical OFDM Systems

Abstract

Radio-over-Fiber (RoF) is offered as an alternative solution for faster data transmission over longer transmission distances in 5G and beyond systems. The use of coherent receivers provides significant gains in optical communication systems which is one of the sub-components of RoF. In this paper, a complex Extreme Learning Machine-based nonlinear equalizer was proposed to compensate for linear and nonlinear effects of the transmission channel for MQAM coherent-OFDM receivers, and performance analyzes were performed with Monte Carlo simulations.

Keywords

• Complex Extreme Learning Machine
• coherent optical OFDM
• fiber nonlinearity.

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