Symbol Classification in Receiver of OFDM Carrier Signal with Deep Learning and Whale Optimization Algorithm

Abstract

Orthogonal Frequency Division Multiplexing (OFDM) remains a cornerstone in modern wireless communication systems, owing to its resilience to multipath fading and spectral efficiency. In OFDM systems, accurate symbol classification is paramount for successful data demodulation. This paper proposes a novel methodology for symbol classification in the receiver of an OFDM carrier signal, using a synergic combination of deep learning and feature selection with the Whale Optimization Algorithm (WOA). The deep learning component, embodied in a convolutional neural network (CNN), is adept at extracting intricate features from the received OFDM symbols, while the WOA facilitates efficient feature selection by optimizing a subset of attributes that contribute most to categorization accuracy. This dual approach not only enhances the discriminative power of the classification model but also reduces the computational complexity by focusing on the most relevant features. Experimental findings confirm the effectiveness of the proposed framework, demonstrating superior symbol classification performance compared to conventional methods. Moreover, the integration of feature selection with the WOA ensures the identification of an optimal subset of features, further improving classification accuracy and generalization capability. This study combines DL with metaheuristic feature selection to improve symbol classification in OFDM receivers, thereby making wireless communication systems more reliable and efficient.

Keywords

• Symbol Classification
• OFDM
• Deep learning
• Wale optimization algorithm

Derin Öğrenme ve Balina Optimizasyon Algoritması ile OFDM Taşıyıcı Sinyal Alıcıda Sembol Sınıflandırma

Abstract

Ortogonal Frekans Bölmeli Çoğullama (OFDM), çok yollu sönümlemeye karşı dayanıklılığı ve spektral verimliliği sayesinde modern kablosuz iletişim sistemlerinin temel taşlarından biri olmaya devam etmektedir. OFDM sistemlerinde, başarılı veri demodülasyonu için doğru sembol sınıflandırması çok önemlidir. Bu makale, Balina Optimizasyon Algoritması (WOA) ile derin öğrenme ve özellik seçiminin sinerjik bir kombinasyonunu kullanarak bir OFDM taşıyıcı sinyalinin alıcısında sembol sınıflandırması için yeni bir metodoloji önermektedir. Bir evrişimli sinir ağında (CNN) somutlaşan derin öğrenme bileşeni, alınan OFDM sembollerinden karmaşık özellikleri çıkarmada ustalaşırken, WOA, kategorizasyon doğruluğuna en çok katkıda bulunan bir alt özellik kümesini optimize ederek verimli özellik seçimini kolaylaştırır. Bu ikili yaklaşım sadece sınıflandırma modelinin ayırt edici gücünü artırmakla kalmaz, aynı zamanda en ilgili özelliklere odaklanarak hesaplama karmaşıklığını da azaltır. Deneysel bulgular, geleneksel yöntemlere kıyasla üstün sembol sınıflandırma performansı göstererek önerilen çerçevenin etkinliğini doğrulamaktadır. Ayrıca, özellik seçiminin WOA ile entegrasyonu, optimum özellik alt kümesinin belirlenmesini sağlayarak sınıflandırma doğruluğunu ve genelleme yeteneğini daha da geliştirmektedir. Bu çalışma, OFDM alıcılarında sembol sınıflandırmasını iyileştirmek için DL ile metasezgisel özellik seçimini birleştirerek kablosuz iletişim sistemlerinin daha güvenilir ve verimli olmasını sağlar.

Keywords

• Sembol Sınıflandırma
• OFDM
• Derin öğrenme
• Wale optimizasyon algoritması

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