“Deep Learning for Next-Generation Wireless Networks Enabling the Transition from 5G to 6G”

Registration ID: IJNRD_325525

Published ID: IJNRD2605708

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M. Francis, M. Varshitha, SK.Reshma, B. Sirisha, K. Bhavya

6G networks, generative models, explainable artificial intelligence (XAI), deep learning, Autonomous Network Management

Intelligent, flexible, and autonomous systems that can manage high performance demands are becoming more and more necessary as communication networks transition from fifth-generation (5G) to sixth-generation (6G). This paper presents a Deep Learning-Driven Artificial Intelligence Framework that uses explainable AI, generative modeling, federated learning, and integrated edge intelligence to enhance 6G network operations. In addition to providing improved scalability, low latency, and increased energy efficiency, the framework guarantees robust data privacy and security and enables real-time decision-making. To balance network accuracy, latency, and energy consumption, we created a multi-objective optimization model. A hybrid deep learning architecture that blends transformer and convolutional layers supports this model. According to our comparative analysis, this framework improves energy efficiency, lowers latency by 90%, and achieves up to 95% accuracy .In order to enhance 6G network operations, this paper presents a Deep Learning-Driven Artificial Intelligence Framework that makes use of explainable AI, federated learning, integrated edge intelligence, and generative modelling .The framework guarantees robust data privacy and security, improves scalability, reduces latency, and increases energy efficiency while enabling real-time decision-making. To balance network accuracy, latency, and energy consumption, we created a multi-objective optimization model. A hybrid deep learning architecture that blends transformer and convolutional layers supports this model. Our comparative analysis demonstrates that this framework outperforms existing and traditional AI models, achieving up to 95% accuracy, 90% latency reduction, and 88% energy efficiency improvement. Explainable and generative AI modules improve the system's interpretability and resilience to shifting network conditions. The foundation for completely intelligent, safe, and sustainable 6G networks is laid by this study, paving the way for future integrations with digital twin and quantum technologies.

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M. Francis, M. Varshitha, SK.Reshma, B. Sirisha, & K. Bhavya (May-2026). “Deep Learning for Next-Generation Wireless Networks Enabling the Transition from 5G to 6G”. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), h102-h112. https://ijnrd.org/papers/IJNRD2605708.pdf

Volume 11 Issue 5, May-2026

Pages : h102-h112

Paper Reg. ID: IJNRD_325525

Published Paper Id: IJNRD2605708

Research Area: Other area not in list

Author Type: Indian Author

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2605708.pdf

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