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CP-AgentNet: Autonomous and Explainable Communication Protocol Design Using Generative Agents

Dae Cheol Kwon, Xinyu Zhang

TL;DR

CP-AgentNet presents a novel framework that uses generative LLM-based agents to autonomously design and adapt communication protocols, addressing DRL drawbacks such as manual architecture tuning, opaque decision-making, and data demands. Through multi-agent role-play, Progressive Strategy Augmentation, Autonomous Strategy Implementation, and supporting tools like a compilation checker and LLM ranker, the approach produces explainable strategies without gradient-based training. The framework yields two use cases, LLMA for MAC and CPTCP for TCP, and demonstrates that these protocols can coexist with heterogeneous nodes and protocols in dynamic environments, achieving near-ideal performance and improved fairness. The work highlights the practical potential of generative agents for scalable, interpretable, and autonomous networking design, while outlining a path toward broader deployment in diverse network scenarios.

Abstract

Although DRL (deep reinforcement learning) has emerged as a powerful tool for making better decisions than existing hand-crafted communication protocols, it faces significant limitations: 1) Selecting the appropriate neural network architecture and setting hyperparameters are crucial for achieving desired performance levels, requiring domain expertise. 2) The decision-making process in DRL models is often opaque, commonly described as a 'black box.' 3) DRL models are data hungry. In response, we propose CP-AgentNet, the first framework designed to use generative agents for developing communication network protocols. This approach addresses these challenges by creating an autonomous system for protocol design, significantly reducing human effort. We developed LLMA (LLM-agents-based multiple access) and CPTCP (CP-Agent-based TCP) for heterogeneous environments. Our comprehensive simulations have demonstrated the efficient coexistence of LLMA and CPTCP with nodes using different types of protocols, as well as enhanced explainability.

CP-AgentNet: Autonomous and Explainable Communication Protocol Design Using Generative Agents

TL;DR

CP-AgentNet presents a novel framework that uses generative LLM-based agents to autonomously design and adapt communication protocols, addressing DRL drawbacks such as manual architecture tuning, opaque decision-making, and data demands. Through multi-agent role-play, Progressive Strategy Augmentation, Autonomous Strategy Implementation, and supporting tools like a compilation checker and LLM ranker, the approach produces explainable strategies without gradient-based training. The framework yields two use cases, LLMA for MAC and CPTCP for TCP, and demonstrates that these protocols can coexist with heterogeneous nodes and protocols in dynamic environments, achieving near-ideal performance and improved fairness. The work highlights the practical potential of generative agents for scalable, interpretable, and autonomous networking design, while outlining a path toward broader deployment in diverse network scenarios.

Abstract

Although DRL (deep reinforcement learning) has emerged as a powerful tool for making better decisions than existing hand-crafted communication protocols, it faces significant limitations: 1) Selecting the appropriate neural network architecture and setting hyperparameters are crucial for achieving desired performance levels, requiring domain expertise. 2) The decision-making process in DRL models is often opaque, commonly described as a 'black box.' 3) DRL models are data hungry. In response, we propose CP-AgentNet, the first framework designed to use generative agents for developing communication network protocols. This approach addresses these challenges by creating an autonomous system for protocol design, significantly reducing human effort. We developed LLMA (LLM-agents-based multiple access) and CPTCP (CP-Agent-based TCP) for heterogeneous environments. Our comprehensive simulations have demonstrated the efficient coexistence of LLMA and CPTCP with nodes using different types of protocols, as well as enhanced explainability.

Paper Structure

This paper contains 45 sections, 15 equations, 11 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Background
  3. System Design
  4. CP-AgentNet Architecture
  5. CP-AgentNet Workflow
  6. Generating Few-Shot Demonstrations
  7. Simulation-Based Demonstration Generation
  8. Heterogeneous Demonstration Generation
  9. Strategy generation
  10. Refining Strategies
  11. Online Adaptation
  12. Autonomous Design
  13. Stability of CP-AgentNet
  14. Querying the LLM with Different Input Orders
  15. Selecting the Best Strategy Using Another LLM as a Judge
  16. ...and 30 more sections

Figures (11)

  • Figure 1: Autonomous and explainable protocol design with stability is achieved via CP-AgentNet
  • Figure 2: The workflow of CP-AgentNet. Left: Offline stage, Right: Online stage.
  • Figure 3: Example prompts for strategy generation: initial strategy (top) and strategy refinement (bottom).
  • Figure 4: An example prompt of PSA.
  • Figure 5: An example prompt of ASI.
  • ...and 6 more figures