Deep Learning Models for Performance Improved 6G Network

The emergence of sixth generation (6G) mobile networks is poised to transform global communication by providing unmatched speed, dependability, and sophisticated network management. This study examines the essential function of AI and machine learning (ML) is in unlocking the whole potential within 6G networks, emphasizing current developments, primary difficulties, and future directions. Machine learning methods are essential for tackling intricate challenges like as distributing resources, energy efficiency, & latency reduction in 6G networks, enabling novel applications such autonomous systems including holographic communication. Notwithstanding the encouraging advancements, obstacles such as the development of scalable machine learning models, the assurance of network security, and the integration of AI-driven solutions into current infrastructures remain. The paper classifies machine learning applications in 6G according to various network topologies and emphasizes unresolved research challenges, especially in improving the trade-off between computational complexity and performance. This study establishes a foundation for future research, highlighting the significance of machine learning in influencing the future development of wireless networks.