Continual Learning of Feedback-based Molecular Communication
For researchers in molecular communication, this work introduces continual learning to the domain, but the results are incremental and domain-specific.
This paper proposes a continual learning-based method for sequential simulation experiments of a feedback-based molecular communication protocol, achieving improved estimation accuracy over a baseline neural network across various computational costs.
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.