6G THz Ağlarında Açıklanabilir ve Güvenilir Yapay Zeka: Zorluklar, Çözüm Yaklaşımları ve Gelecek Perspektifleri

Abstract

6G Terahertz (THz) haberleşme teknolojileri, yüksek veri iletim hızları, ultra düşük gecikme süresi ve yoğun bağlantı kapasitesi sunarak gelecek nesil kablosuz iletişim ağlarının temel yapı taşlarından biri olarak öne çıkmaktadır. Bu ağların yüksek performanslı, dinamik ve esnek bir şekilde yönetilmesinde yapay zekâ (YZ) hayati bir rol üstlenmektedir. Ancak YZ modelleri, karar alma süreçlerinin opak yapısı nedeniyle özellikle güvenlik, şeffaflık ve güvenilirlik açısından önemli zorluklar barındırmaktadır. Bu derleme çalışması, 6G THz ağları için özelleştirilmiş Açıklanabilir Yapay Zekâ (XAI) ve Güvenilir Yapay Zekâ (Trustworthy AI) yaklaşımlarını kapsamlı bir şekilde incelemektedir. Çalışma, hüzmeleme, kaynak tahsisi ve kanal modelleme gibi temel kullanım senaryolarında YZ’nin rolünü değerlendirerek başlamaktadır. Ardından, SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-Agnostic Explanations) ve dikkat (attention) tabanlı görselleştirme gibi yaygın XAI teknikleri ele alınmakta ve bu yöntemlerin karmaşık ağ mimarilerinde uygulanabilirliği tartışılmaktadır. Bununla birlikte, düşman saldırılar (adversarial attacks), veri zehirleme (data poisoning) ve mahremiyet ihlalleri gibi kritik güvenlik tehditleri incelenmekte; model dayanıklılığı ve hesap verebilirliğini artırmaya yönelik mevcut çözüm önerileri değerlendirilmektedir. Son olarak, performans–açıklanabilirlik dengesi ile ilişkili temel zorluklar tanımlanmakta ve 6G ağlarında güvenli, şeffaf ve regülasyonlarla uyumlu YZ sistemlerinin geliştirilmesine yönelik gelecek araştırma alanları ortaya konulmaktadır.

Keywords

• 6G THz ağlar
• açıklanabilir yapay zeka (XAI)
• güvenilir yapay zeka
• yeni nesil kablosuz ağlar

Explainable and Trustworthy Artificial Intelligence in 6G THz Networks: Challenges, Solutions, and Future Perspectives

Abstract

6G Terahertz (THz) communication technologies are emerging as one of the fundamental cornerstones of next-generation wireless networks, offering high data transmission speeds, ultra-low latency, and dense connectivity. Artificial intelligence (AI) plays a critical role in managing these networks in a high-performance, dynamic, and flexible manner. However, AI models pose significant challenges, especially regarding security, transparency and reliability, due to their opaque decision-making processes. This review provides a comprehensive examination of Explainable Artificial Intelligence (XAI) and Trustworthy AI approaches tailored for 6G THz networks. The study begins by evaluating the role of AI in key use cases such as beamforming, resource allocation, and channel modeling. Then it explores popular XAI techniques, including SHAP(SHapley Additive exPlanations), LIME (Local Interpretable Model-Agnostic Explanations), and attention-based visualization, and discusses their applicability within complex network architectures. Moreover, it investigates critical security threats, such as adversarial attacks, data poisoning, and privacy breaches, and reviews existing solutions aimed at enhancing model robustness and accountability. Finally, this work identifies key challenges associated with the performance-explainability trade-off and outlines promising directions for future research in the development of secure, transparent, and regulation-compliant AI systems for 6G networks.

Keywords

• 6G THz networks
• explainable artificial intelligence (XAI)
• trustworthy artificial intelligence
• next generation wireless networks

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