ComAgent: Multi-LLM Based Agentic AI Empowered Intelligent Wireless Networks

Li H, Ming X, Wang K, Schober R, Kim DI, Guan YL (2026)

Publication Type: Journal article

Publication year: 2026

Journal

IEEE Wireless Communications IEEE

DOI: 10.1109/MWC.2026.3711801

Abstract

Emerging wireless networks such as the sixth-generation (6G) mobile systems are expected to operate over highly heterogeneous infrastructures with rapidly evolving service demands, thereby increasing the dependence on large-scale, constraint-coupled, cross-layer optimization for network design and operation. However, the trend leads to a fundamental bottleneck: Converting high-level intents into mathematically consistent formulations, implementable algorithms, and reproducible simulations remains predominantly human-driven, time-intensive, and error-prone. While large language models (LLMs) provide a natural-language interface for intent interpretation and rapid prototyping, monolithic LLM-based pipelines are often limited by insufficient domain grounding, weak constraint awareness, and a lack of execution-based verification and self-correction. These limitations motivate a shift towards agentic AI, where problem-solving is realized through iterative decomposition, explicit planning, tool-integrated execution, and reflection driven by feedback. In this paper, we present ComAgent, a multi-LLM based agentic AI framework that coordinates specialized agents for literature searching, planning, coding, and solution scoring within a closed-loop Perception–Planning–Action–Reflection cycle, turning user intents into solver-ready formulations and reproducible simulation pipelines while continuously self-correcting logical and feasibility errors. We demonstrate the efficacy of ComAgent through two distinct evaluations. In a non-trivial beamforming optimization case study, ComAgent autonomously perceives the problem, designs an algorithm, and generates solutions that achieve a performance comparable to that of expert-designed baselines. Furthermore, on a diverse set of generic wireless tasks, ComAgent outperforms monolithic LLMs. The numerical results demonstrate the potential of the proposed agentic AI framework for various emerging wireless networks.

Authors with CRIS profile

Robert Schober Lehrstuhl für Digitale Übertragung (IDC)

Involved external institutions

Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH) SE Sweden (SE) National University of Singapore (NUS) SG Singapore (SG) Brunel University London GB United Kingdom (GB) Sungkyunkwan University (SKKU) KR Korea, Republic of (KR)

How to cite

APA:

Li, H., Ming, X., Wang, K., Schober, R., Kim, D.I., & Guan, Y.L. (2026). ComAgent: Multi-LLM Based Agentic AI Empowered Intelligent Wireless Networks. IEEE Wireless Communications. https://doi.org/10.1109/MWC.2026.3711801

MLA:

Li, Haoyun, et al. "ComAgent: Multi-LLM Based Agentic AI Empowered Intelligent Wireless Networks." IEEE Wireless Communications (2026).

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