ComLLM: Toward A General Large Language Model for Semantic-Aware Network Optimization

Wencan Mao

doi:10.1109/mnet.2026.3716787

ComLLM: Toward A General Large Language Model for Semantic-Aware Network Optimization

2026-08-04

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DOI：10.1109/mnet.2026.3716787

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Optimization, which lies at the core of network operations, is usually associated with extensive expert knowledge and manual overhead, which impedes its efficient implementation. To address this issue, this study illustrates the potential of a paradigm shift from algorithmic solvers to semantic agents for network optimization. Specifically, we envision a generalized Large Language Model (LLM)-based framework, called ComLLM, for addressing fundamental problems in mobile communication networks. Without explicit mathematical problem formulation, ComLLM can understand the semantic context (i.e., high-level, human-interpretable goals and constraints) and autonomously call incorporated prediction modules to generate adaptive optimization decisions. A case study on mobile edge computing demonstrates that ComLLM effectively performs joint data transmission rate prediction and autonomous vehicle task scheduling. ComLLM can be used for minimizing end-to-end latency or maximizing revenue. Furthermore, the paper provides insights into ComLLM and discusses open challenges.

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Modeling

Optimization

Educational institutions

Large language models

Scheduling

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ComLLM: Toward A General Large Language Model for Semantic-Aware Network Optimization

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