用持续学习提升分子通信反馈协议的性能预测精度。
Continual Learning of Feedback-based Molecular Communication
- 基于持续学习设计可增量更新的性能估计算法。
- 在不同计算成本下均显著提升神经网络估计准确率。
- 适用于需要长期演进的分子通信系统研究者。
本文提出并评估了一种新的性能估计方法，利用持续学习（CL）算法对基于反馈的分子通信协议进行连续仿真实验。随着协议在不同实验场景中被逐次考察，所提出的基于持续学习的性能估计算法能够增量式地学习一系列未经历过的估计任务，同时不损害已学任务的性能。该方法通过在标准神经网络架构上定制损失函数中的正则化与回放策略实现。实验结果表明，所提估计算法能有效处理连续的仿真数据流，并在多种计算成本下提升基线神经网络的估计精度。本文贡献在于揭示了持续学习在分子通信领域的重要意义。
原文摘要 · Abstract (English)
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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