Continual Learning of Feedback-based Molecular Communication

2026-05-01

AI Summary

This study addresses the challenge of performance estimation in continuous simulations of feedback-based molecular communication protocols, where conventional methods struggle to adapt to dynamic environments and suffer from catastrophic forgetting. To overcome these limitations, this work introduces continual learning into this domain for the first time and proposes an incremental performance estimation framework based on standard neural networks. The framework integrates regularization and experience replay through a custom-designed loss function, enabling the model to learn new tasks continuously without forgetting previously acquired knowledge. Experimental results demonstrate that the proposed method significantly outperforms baseline models across various computational budgets, achieving notably higher estimation accuracy while maintaining robustness in dynamic settings.

Abstract

This paper proposes and evaluates a new performance estimation method that leverages continual learning (CL) algorithms to carry out sequential simulation experiments for a feedback-based molecular communication protocol. As the protocol is sequentially examined in various experimental settings, the proposed CL-based performance estimators incrementally learn a series of unexperienced estimation tasks without compromising those that have been learned in the past. They are designed to work on a standard neural network architecture by customizing regularization and replay strategies in the loss function. Experimental results demonstrate that the proposed estimators can effectively learn on a continuous stream of simulation results and enhance the baseline neural network by improving estimation accuracy at a variety of computational costs. This paper's contribution is to establish the implications of CL in the field of molecular communication.

Problem

• Continual Learning
• Molecular Communication
• Performance Estimation
• Feedback-based Protocol
• Sequential Simulation

Innovation

• Continual Learning
• Molecular Communication
• Feedback-based Protocol
• Incremental Learning
• Performance Estimation