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WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks

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arxiv 2409.07964 v1 pith:OQ3LEC4K submitted 2024-09-12 cs.NI cs.AIcs.LG

Jingwen Tong , Jiawei Shao , Qiong Wu , Wei Guo , Zijian Li , Zehong Lin , Jun Zhang This is my paper · ORCID
classification cs.NIcs.AIcs.LG
keywords networkswirelessagentwirelessadvancedagentscapablechallengeseffectively
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Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce WirelessAgent, a novel approach leveraging large language models (LLMs) to develop AI agents capable of managing complex tasks in wireless networks. It can effectively improve network performance through advanced reasoning, multimodal data processing, and autonomous decision making. Thereafter, we demonstrate the practical applicability and benefits of WirelessAgent for network slicing management. The experimental results show that WirelessAgent is capable of accurately understanding user intent, effectively allocating slice resources, and consistently maintaining optimal performance.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments

    eess.SY 2025-02 conditional novelty 6.0 of 10

    A prompt-tuned LLM with external score feedback solves OFDMA and MU-MIMO resource allocation under QoS and queue constraints, and switches objectives without retraining.

  2. On the Convergence of Large Language Model Optimizer for Black-Box Network Management

    cs.IT 2025-07 reject novelty 4.0 of 10

    The paper claims a first convergence proof for LLM-based black-box optimizers, but the key lemma is proven by assertion rather than derivation.

  3. Joint User Association and Beamforming Design for ISAC Networks with Large Language Models

    cs.IT 2025-06 conditional novelty 4.0 of 10

    A GPT-o1-driven user association step combined with convex beamforming achieves near-optimal sum rate in a small multi-base-station ISAC network.

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