Roles of Artificial Intelligence in Non-Terrestrial Network Communication Management

AI-Driven Mobility Management in 5G and 6G Wireless Networks: A Survey

A comprehensive survey of AI-enabled mobility management strategies for 5G, Beyond 5G, and upcoming 6G networks, with particular attention to HO optimization and load balancing is presented.

Artificial Intelligence-Driven Assessment and Performance Evaluation of Fifth-Generation (5G) Wireless Communication Networks

The findings revealed that AI enhances service reliability, reduces operational costs, minimizes latency, improves throughput, and supports proactive network management while ensuring compliance with Quality-of-Service requirements.

AI-Native Handover Management for 6G Networks Leveraging O-RAN Architecture

This paper places the terrestrial-satellite UAV handover decision inside the near-real-time RIC as an xApp, using a deep reinforcement learning (DRL) framework that combines a Deep Q-Network (DQN) with Prioritized Experience Replay (PER) and a reward shaped to weigh the long-term cost of switching against the reliabili...

AI-Driven Resource Allocation in 6G: A Reinforcement Learning Approach for Sustainable and Energy-Efficient Network Operation

Cross-Layer Energy Management in Embedded AIoT Sensor Networks: A Structured Review, Full-Cycle Energy Model, and Break-Even Analysis

The synthesis shows that acquisition, Tiny Machine Learning (TinyML) inference, communication, protocol-state overhead, adaptive sensing, and energy harvesting are commonly optimized separately, while field-validated joint control remains limited.

Adaptive Spectrum Allocation for Low Latency Vehicular Networks Using Deep Reinforcement Learning

An Adaptive Deep Reinforcement Learning (ADRL) based dynamic spectrum allocation framework for AVNs can ensure efficient spectrum allocation and reliable communication in a fast-growing network density and degraded channel environment and has stable convergence characteristics in its training behavior.