Neural Network Detection of Data Sequences in Communication Systems

Understand

We consider detection based on deep learning, and show it is possible to train detectors that perform well without any knowledge of the underlying channel models.

• Moreover, when the channel model is known, we demonstrate that it is possible to train detectors that do not require channel state information (CSI).
• In particular, a technique we call a sliding bidirectional recurrent neural network (SBRNN) is proposed for detection where, after training, the detector estimates the data in real-time as the signal stream arrives at the receiver.
• We evaluate this algorithm, as well as other neural network (NN) architectures, using the Poisson channel model, which is applicable to both optical and molecular communication systems.