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arXiv · 2605.01020

Continual Learning of Feedback-based Molecular Communication

Siddhant Setia·Junichi Suzuki·Tadashi Nakano
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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.

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Siddhant Setia, Junichi Suzuki, Tadashi Nakano. 2026-05-01. Continual Learning of Feedback-based Molecular Communication. https://arxiv.org/abs/2605.01020

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