Analysis on 6G Networks Using AI Techniques in WSN to Improvise QoS

Abstract

In WSNs, attention is given to evaluating the first generation (1G) networks to 6G networks. AI techniques were applied to achieve network intelligence, closed-loop optimization, and intelligent wireless communication for 6G networks. DRL was adopted to preserve reliable wireless connectivity for UAV-enabled networks by learning the environmental dynamics. Hence, it is promising to adopt AI to 6G networks to optimize the network architecture and improve network performance. 6G networks utilize different spectrum bands to support high data rates. In 6G networks, the massive amounts of collected data and complex network architectures pose challenges for AI-enabled learning and training processes. Limited computing resources may be insufficient to process massive high dimensional data to meet the training accuracy rate, robustness, scalability, and flexibility of learning frameworks, which are crucial aspects for providing high-quality services in real-world dynamic networks. Thus, designing robust, scalable, and flexible learning frameworks for 6G networks is still an open issue.