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WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

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arxiv 2405.17053 v2 pith:3BPA4NHP submitted 2024-05-27 cs.NI cs.AIcs.LG

Jiawei Shao , Jingwen Tong , Qiong Wu , Wei Guo , Zijian Li , Zehong Lin , Jun Zhang This is my paper · ORCID
classification cs.NIcs.AIcs.LG
keywords wirelesswirelessllmknowledgellmscommunicationlanguagenetworkschallenges
verification ladder T0 review T1 audit T2 compute T3 formal
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The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed, configured, and managed. Recent advancements in Large Language Models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. However, existing studies on LLMs for wireless systems are limited to a direct application for telecom language understanding. To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks. We first identify three foundational principles that underpin WirelessLLM: knowledge alignment, knowledge fusion, and knowledge evolution. Then, we investigate the enabling technologies to build WirelessLLM, including prompt engineering, retrieval augmented generation, tool usage, multi-modal pre-training, and domain-specific fine-tuning. Moreover, we present three case studies to demonstrate the practical applicability and benefits of WirelessLLM for solving typical problems in wireless networks. Finally, we conclude this paper by highlighting key challenges and outlining potential avenues for future research.

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Cited by 9 Pith papers

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

  1. Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization

    eess.SP 2026-07 reject novelty 6.0 of 10

    SigMap combines cycle-adaptive masked CSI pre-training with 3D-map soft prompts to achieve strong few-shot wireless localization, though the advertised zero-shot claim is not supported by its own protocol.

  2. When Adaptive Rewards Hurt: Causal Probing and the Switching-Stability Dilemma in LLM-Guided LEO Satellite Scheduling

    cs.AI 2026-04 conditional novelty 6.0 of 10

    Near-constant reward weights outperform carefully tuned dynamic weights in PPO LEO beam scheduling because weight switching restarts value-function convergence.

  3. NextG-GPT: Leveraging GenAI for Advancing Wireless Networks and Communication Research

    cs.ET 2025-05 conditional novelty 5.0 of 10

    A RAG-enhanced LLM assistant for wireless research testbeds is built and evaluated, with LLaMa3.1-70B scoring best, though the abstract mislabels a faithfulness score as correctness.

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

    cs.NI 2025-09 conditional novelty 4.0 of 10

    A survey that organizes LLM and AI reasoning methods into a taxonomy and maps them onto the physical, link, network, transport, and application layers of wireless networks.

  5. Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial

    cs.NI 2025-09 conditional novelty 4.0 of 10

    A survey and tutorial that organizes LLM-enabled wireless network optimization into formulation, solution, and verification stages, with case studies drawn from the authors' own prior papers.

  6. A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication

    eess.SP 2025-06 conditional novelty 4.0 of 10

    This work proposes a unified ILAC framework enhanced by large AI models and hyperdimensional computing, with a cost-to-performance optimization case study solved by Dinkelbach and alternating optimization.

  7. Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration

    eess.SP 2025-06 conditional novelty 4.0 of 10

    The paper proposes a systematic classification and two roadmaps for using foundation models (LLMs and wireless foundation models) to design Synesthesia of Machines systems for 6G, with preliminary case-study evidence ...

  8. ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing

    cs.LG 2025-05 reject novelty 4.0 of 10

    ORAN-GUIDE couples a domain-specific LLM prompt generator with a frozen GPT-2 encoder and learnable prompt tokens to improve multi-agent SAC sample efficiency in O-RAN slicing.

  9. Large Language Models-Empowered Wireless Networks: Fundamentals, Architecture, and Challenges

    cs.NI 2025-06 conditional novelty 3.0 of 10

    A magazine-style position paper that frames LLM-native wireless systems and illustrates a modified DDQN scheme that appears to improve convergence over plain DDQN in simulation.

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