CP-AgentNet: Autonomous and Explainable Communication Protocol Design Using Generative Agents
Dae Cheol Kwon, Xinyu Zhang · 2025
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 to employ generative agents as autonomous decision-makers for communication protocol design. This approach addresses these challenges by creating an autonomous system for protocol design, significantly reducing human effort. As practical use cases, we developed LLMA (LLM-agents-based multiple access) and CPTCP (CP-Agent-based TCP) tailored 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.