AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives

2025-09-11

AI Summary

Conventional deep learning lacks structured, multi-step reasoning capabilities for wireless network optimization. Method: This paper proposes a novel AI reasoning paradigm tailored to communication networks, featuring (i) a hierarchical reasoning framework spanning the physical to application layers; (ii) the first taxonomy of AI reasoning tasks specifically designed for wireless systems; and (iii) a large language model (LLM)-based agent endowed with tool invocation, long-horizon planning, and memory mechanisms to enable low-intervention, cross-layer collaborative network optimization. Contribution/Results: We systematically analyze inference challenges at each layer and provide interpretable, principled solutions. The work establishes a theoretical foundation, practical technical pathway, and scalable architectural blueprint for intelligent wireless networks—significantly enhancing autonomous decision-making capability and environmental adaptability.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Artificial Intelligence (AI) techniques play 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, and application layers. For each part, we identify key challenges and illustrate how AI reasoning methods can improve AI-based wireless communication performance. Finally, we discuss key research directions for AI reasoning toward future wireless communication networks. By combining insights from both communications and AI, this survey aims to chart a path for integrating reasoning techniques into the next-generation wireless networks.

Problem

• Addresses limitations of traditional AI in wireless network optimization
• Explores reasoning-enabled AI for complex multi-step decision problems
• Investigates LLM-based autonomous control for dynamic network operations

Innovation

• LLM-based agents for autonomous cross-layer control
• Combining reasoning with long-term planning and memory
• AI reasoning techniques for multi-layer network optimization