AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives
Haoxiang Luo, Yu Yan, Yanhui Bian, Wenjiao Feng, Ruichen Zhang, Yinqiu Liu, Jiacheng Wang, Gang Sun, Dusit Tao Niyato, Hongfang Yu, Abbas Jamalipour, Shiwen Mao · ACM Computing Surveys · 2026
Artificial Intelligence (AI) plays a pivotal role in optimizing wireless communication networks. However, traditional deep learning approaches often act as closed boxes, lacking the structured reasoning abilities needed to tackle complex, multi-step decision problems. This survey provides a comprehensive review and outlook of reasoning-enabled AI in wireless communication networks, with a focus on Large Language Models (LLMs) and other advanced reasoning paradigms. In particular, LLM-based agents can combine reasoning with long-term planning, memory, tool utilization, and autonomous cross-layer control to dynamically optimize network operations with minimal human intervention. We begin by outlining the evolution of intelligent wireless networking and the limitations of conventional AI methods. We then introduce emerging AI reasoning techniques. Furthermore, we establish a classification system applicable to wireless network tasks. We also present a layer-by-layer examination for AI reasoning, covering the physical, data link, network, transport, application, and security layers. For each part, we identify key challenges and illustrate how AI reasoning can improve wireless performance. Meanwhile, we also provide the actual deployment and cost analysis of AI reasoning. Finally, we discuss research directions for AI reasoning toward future wireless communication networks. By combining insights from both communications and AI, this survey aims at charting a path for integrating reasoning techniques into the next-generation wireless networks.