Intelligent and secure mobility management for 6g networks: A systematic review of AI-driven and physical-layer solutions

The development of Sixth-Generation (6G) wireless networks introduces new security and mobility management challenges due to dense deployments, heterogeneous multi-access systems, extensive reliance on Artificial Intelligence (AI), and the integration of non-terrestrial networks. This work investigates intelligent and secure mobility management for 6G and beyond through a systematic literature review. Following the PRISMA guidelines, 301 relevant studies published between 2010 and 2024 were identified across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. The review evaluates current AI methods, including Deep Learning (DL), Reinforcement Learning (RL), and federated learning, with respect to their use in mobility prediction, handover optimization, resource allocation, and energy-efficient network operation. It also examines Physical-Layer Security (PLS) mechanisms that complement traditional upper-layer cryptographic techniques to mitigate jamming, eavesdropping, and adversarial attacks while controlling computational overhead. The analyzed studies are systematically organized by linking performance targets such as Quality of Service (QoS), Quality of Experience (QoE), latency, reliability, and scalability to five key 6G enabling technologies/functions: reconfigurable intelligent surfaces, Millimeter-Wave (mmWave) communications, massive Multiple-Input Multiple-Output (MIMO) systems, mobile edge computing, and AI-assisted channel estimation, which provides the Channel State Information (CSI) required by secure beamforming, Intelligent Reflecting Surface (IRS) control, and mobility-aware PLS. The findings show that existing systems remain limited in their ability to guarantee secure and dependable connectivity under high-speed and extreme-mobility conditions. The review highlights three critical open challenges: protecting AI model training against physical and digital attacks, ensuring that PLS schemes operate without disrupting network control, and maintaining robust mobility management under stringent performance constraints. Based on these insights, the paper proposes design principles to guide future research on AI-based, security-focused mobility management frameworks that satisfy 6G performance requirements.