Future of connectivity: a comprehensive review of innovations and challenges in 7G smart networks

Abstract

The evolution from 1G to 6G networks has transformed global communication, progressing from basic voice calls in 1G to the immersive, AI-enabled experiences of 6G. As emerging AI-driven applications like autonomous systems, the Internet of Everything (IoE), and immersive technologies demand unprecedented capabilities, 7G networks are set to redefine connectivity by overcoming the limitations of earlier generations. This paper comprehensively reviews the innovations and challenges in 7G networks, focusing on integrating advanced AI and machine learning paradigms such as meta-learning, incremental learning, distributed intelligence, and reinforcement learning to enhance adaptability, resource allocation, and edge performance. The review also examines the role of Large Language Models (LLMs) in enabling real-time actionable intelligence and optimizing edge devices within 7G. The paper highlights the use of technologies, including blockchain for decentralized security, quantum computing for robust encryption, terahertz communication for ultra-fast data transfer, zero-energy solutions for sustainability, and generative AI for intelligent network optimization and automation. By addressing these challenges and exploring cutting-edge strategies, this paper envisions 7G networks as the foundation for a secure, intelligent, and sustainable digital future, equipped to combat emerging cyber warfare threats, enhance resilience against technological disruptions, and support innovations across smart cities, autonomous systems, healthcare, and industrial IoT.