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LLM-Empowered Resource Allocation in Wireless Communications Systems

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arxiv 2408.02944 v1 pith:4PCDD3O7 submitted 2024-08-06 eess.SP cs.AIcs.SYeess.SY

classification eess.SPcs.AIcs.SYeess.SY
keywords allocationresourcewirelessllm-basedllmssystemscommunicationefficiency
verification ladder T0 review T1 audit T2 compute T3 formal
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The recent success of large language models (LLMs) has spurred their application in various fields. In particular, there have been efforts to integrate LLMs into various aspects of wireless communication systems. The use of LLMs in wireless communication systems has the potential to realize artificial general intelligence (AGI)-enabled wireless networks. In this paper, we investigate an LLM-based resource allocation scheme for wireless communication systems. Specifically, we formulate a simple resource allocation problem involving two transmit pairs and develop an LLM-based resource allocation approach that aims to maximize either energy efficiency or spectral efficiency. Additionally, we consider the joint use of low-complexity resource allocation techniques to compensate for the reliability shortcomings of the LLM-based scheme. After confirming the applicability and feasibility of LLM-based resource allocation, we address several key technical challenges that remain in applying LLMs in practice.

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Forward citations

Cited by 8 Pith papers

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

  1. LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach

    cs.AI 2025-10 conditional novelty 5.0 of 10

    An LLM-based multi-agent reinforcement learning framework that models uplink MAC scheduling as a Stackelberg game reports 77.6% higher throughput and 65.2% better fairness in simulation.

  2. BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization

    eess.SY 2025-09 conditional novelty 5.0 of 10

    A BERT-based transformer, BERT4beam, learns to output beamforming vectors from CSI and achieves near-SCA performance across multiple MU-MISO tasks and system scales.

  3. Large Language Model Enabled Multi-Task Physical Layer Network

    cs.IT 2024-12 conditional novelty 5.0 of 10

    A single fine-tuned LLM backbone with task-specific encoders, decoders, and text prompts performs three physical-layer wireless tasks with accuracy close to dedicated single-task networks.

  4. Towards Wireless Native Big AI Model: The Mission and Approach Differ From Large Language Model

    eess.SP 2024-12 conditional novelty 5.0 of 10

    A position paper proposing that wireless intelligence should be built natively from radio physics, not transferred from large language models.

  5. Harnessing Large Language Models for Intelligent Resource Allocation in the Internet of Everything

    cs.NI 2026-07 reject novelty 4.0 of 10

    LLM-PRO, a hybrid LLM + Q-learning scheduler with an external evaluator, is claimed to improve IoE resource allocation convergence speed, latency, and energy consumption over QL, PSO, and DQN.

  6. A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving

    cs.NE 2025-09 conditional novelty 4.0 of 10

    A literature survey that classifies LLM-based optimization research into modeling and solving, with solving divided into LLMs as optimizers, low-level components, and high-level managers.

  7. A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

    cs.NI 2024-12 conditional novelty 4.0 of 10

    A systematic survey of 108 papers classifies how large language models are used for communication network and service management across four network domains.

  8. From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications

    cs.AI 2025-05 conditional novelty 2.0 of 10

    This paper is a broad tutorial on applying LAMs and agentic AI to 6G, largely restating existing research rather than introducing new results.

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