Survey On Adaptive AI Techniques For Qos And Privacy Preservation In Multi-User 6G Networks

Abstract

The emergence of sixth-generation (6G) wireless networks promises unprecedented advancements in communication technologies, offering ultra-low latency, high throughput, and massive connectivity—especially for the Internet of Things (IoT) ecosystem. However, delivering consistent Quality of Service (QoS) in ultra-dense, heterogeneous, and security-sensitive 6G environments presents significant challenges. Orthogonal Frequency Division Multiple Access (OFDMA), though foundational, faces limitations in dynamic spectrum allocation, latency control, and interference mitigation under such complex scenarios. This survey explores the role of Artificial Intelligence (AI), particularly Deep Reinforcement Learning (DRL), in addressing these challenges by enabling adaptive optimization of transmission parameters, intelligent beam forming, and real-time traffic scheduling. Furthermore, the survey investigates AI-enhanced security and privacy-preserving mechanisms, which are crucial to mitigate threats from untrusted devices while maintaining QoS integrity. We examine recent advancements in integrated resource management, beam forming control, anomaly detection, and secure spectrum allocation across multi-user 6G IoT frameworks. By systematically analyzing existing methods, their advantages, and their limitations, this work highlights critical research gaps—including the lack of unified, real-time adaptive frameworks for joint QoS and security optimization. The survey concludes by outlining future directions aimed at developing scalable, intelligent, and robust AI-driven architectures to meet the demanding requirements of next-generation IoT-driven 6G communications.