Artificial intelligence, big data analytics, and deep learning for 6G communication networks: Challenges, applications, and future directions

The fast development of 6G communication systems has posed serious challenges with management of ultra-density connections, massive traffic, and extremely evolving wireless environments that demand new solutions with the help of Artificial Intelligence, Big Data Analytics, and Deep Learning. The conventional network management techniques cannot be used in supporting AI-native networks, terahertz communication, ultra-massive MIMO or integrated sensing and communication which are presumed to be key technologies of beyond-5G systems. The paper offers a detailed literature review concerning intelligent wireless systems, edge AI, federated learning, and data-driven networking. The review examines new applications, architecture, and technical issues towards integrating machine learning into wireless, digital twin networks, and self-organizing networks into future communication infrastructures. Findings suggest that low latency, high reliability, and energy-efficient communication in the next-generation networks can only be achieved through the use of AI-based optimization, autonomous control of networks, and real-time analytics made possible by the big data analytics. The results also indicate the growing research interest in network automation, smart connectivity and intelligent network management especially in the support of Internet of Everything, immersive services and large-scale cyber-physical systems. Nevertheless, other problems, such as computational complexity, security, privacy, interoperability, and scalability are no longer mere obstacles to implementation. The review believes that the deep learning, AI-driven optimization, and distributed analytics will be at the core of the future wireless systems and that intelligent, autonomous, and adaptive communication architectures will be a research focus of designing the 6G ecosystems.