In this letter, a deep-learning based method is proposed for signal sequence detection. A novel neural network (NN) architecture, in communications systems called Cooperative and Time-varying Bidirectional Recurrent Neural Network (CTBRNN), is developed, which learns from the training data and estimates the transmitted signal sequence without knowing the underlying channel model. Furthermore, we develop a chemical communication experimental platform to collect real data, which is used to train the NN and evaluate the performance of the developed detector. Experimental results demonstrate that, the proposed detection method outperforms the existing NN-based and NN-free candidate solutions in terms of the detection accuracy.

中文翻译：

---

CTBRNN：一种用于通信系统的新型基于深度学习的信号序列检测器

在这封信中，提出了一种基于深度学习的信号序列检测方法。开发了一种在通信系统中称为协作和时变双向循环神经网络 (CTBRNN) 的新型神经网络 (NN) 架构，该架构从训练数据中学习并在不知道底层信道模型的情况下估计传输的信号序列。此外，我们开发了一个化学通信实验平台来收​​集真实数据，用于训练神经网络并评估开发的检测器的性能。实验结果表明，所提出的检测方法在检测精度方面优于现有的基于神经网络和无神经网络的候选解决方案。