  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.
