Edge Computing-Assisted AI for Wireless Communication: Cross-Scenario Integration, Security, and Resource Optimization

Abstract

With the rapid deployment of 5G and the appearance of 6G, wireless communication is transitioning from connection-oriented to intelligent service-oriented networks. Edge computing and artificial intelligence (AI) have been generally recognised as critical enablers for solving latency, scalability, and security barriers, but existing research is fragmented and scenario-specific. UAV networks, self-driving cars, and smart cities are the three sample domains in which this study provides a comprehensive review and comparative analysis of edge-AI integration. The results show that although AI-driven solutions increase adaptability and decision-making accuracy, edge computing enhances real-time responsiveness, resource efficiency, and data security. Large-scale implementation is hampered by methodological and practical limitations, such as a dependence on simulation studies, a lack of cross-scenario collaboration, and a lack of uniform standards, as the review finds. Furthermore, because it is a literature-based study, this research is limited in its coverage of new application areas outside of the three main scenarios and does not offer empirical confirmation. Therefore, future studies should concentrate on robust security frameworks, adaptive scheduling techniques, lightweight cross-scenario AI models, and standardised assessment systems that are backed by practical testing. These directions will be crucial for realizing secure, scalable, and intelligent wireless communication in the 6G era.