Sliding Bidirectional Recurrent Neural Networks for Sequence Detection in Communication Systems

TL;DR

This paper introduces a sliding bidirectional RNN for real-time sequence detection in communication systems with unknown channels, demonstrating superior performance on experimental chemical communication data.

Contribution

It presents a novel deep learning-based detection method that does not require prior channel knowledge, applicable to complex or unknown communication channels.

Findings

SBRNN outperforms previous detection algorithms.

Deep learning improves detection accuracy in unknown channels.

Experimental results validate the effectiveness of the proposed method.

Abstract

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.