用于通信系统序列检测的滑动双向递归神经网络
Sliding Bidirectional Recurrent Neural Networks for Sequence Detection in Communication Systems
摘要：
通信系统的设计和分析通常依赖于描述底层通信信道的数学模型的开发。然而，在某些系统中，例如化学信号用于信息传递的分子通信系统，潜在的信道模型是未知的。在这些情况下，需要一种全新的设计和分析方法。在这项工作中，我们专注于通信系统的一个重要方面，即检测算法，并证明通过使用深度学习工具，可以在不了解底层信道模型的情况下训练性能良好的检测器。我们提出了一种技术，我们称之为滑动双向递归神经网络（SBRNN）的实时序列检测。我们评估该算法使用的实验数据，收集的化学通信平台，其中的通道模型是未知的，难以建模分析。我们表明，深度学习算法的性能明显优于以前的作品中提出的检测器，SBRNN优于这项工作中考虑的其他技术。
英文摘要：
The design and analysis of communication systems typically rely on the development of mathematical models that describe the underlying communication channel. However, in some systems, such as molecular communication systems where chemical signals are used for transfer of information, the underlying channel models are unknown. In these scenarios, a completely new approach to design and analysis is required. In this work, we focus on one important aspect of communication systems, the detection algorithms, and demonstrate that by using tools from deep learning, it is possible to train detectors that perform well without any knowledge of the underlying channel models. We propose a technique we call sliding bidirectional recurrent neural network (SBRNN) for real-time sequence detection. We evaluate this algorithm using experimental data that is collected by a chemical communication platform, where the channel model is unknown and difficult to model analytically. We show that deep learning algorithms perform significantly better than a detector proposed in previous works, and the SBRNN outperforms other techniques considered in this work.
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