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Zero-shot performance analysis of large language models in sumrate maximization.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Telecommunications Engineering

Background:

  • Large language models (LLMs) are increasingly versatile, impacting diverse fields including natural language processing.
  • In networking, LLMs offer potential for resource optimization and sharing, particularly for complex tasks like sumrate maximization.
  • Traditional algorithms for sumrate maximization can be difficult to implement and understand.

Purpose of the Study:

  • To evaluate the feasibility of using LLMs for sumrate maximization in networking without prior algorithmic knowledge.
  • To assess the performance of LLMs in a zero-shot setting for network resource optimization.
  • To compare LLM performance against state-of-the-art approaches for sumrate maximization.

Main Methods:

  • A zero-shot analysis was conducted on LLMs to assess their suitability for sumrate maximization.
  • Experiments involved varying the number of cellular users and device-to-device (D2D) pairs.
  • The efficiency of LLMs was measured against established methods for sumrate maximization.

Main Results:

  • The GPT model achieved a maximum average efficiency of approximately 58% for sumrate maximization.
  • Performance varied based on the combinations of cellular users and D2D pairs tested.
  • Some LLM variants demonstrated limitations with numerical and structural data in their current state.

Conclusions:

  • LLMs, particularly GPT, show promise for simplifying sumrate maximization in networking.
  • Further research and parameter fine-tuning are necessary for optimal LLM application to numerical network data.
  • LLMs can potentially reduce the complexity associated with implementing advanced networking algorithms.