Neural Network Detection of Data Sequences in Communication Systems
This addresses the challenge of reliable data detection in communication systems, especially for domains like optical and molecular communications where channel models are difficult to model analytically, though it is incremental as it builds on existing neural network approaches.
The paper tackles the problem of detecting data sequences in communication systems without requiring knowledge of the underlying channel models or channel state information, proposing a sliding bidirectional recurrent neural network (SBRNN) that achieves better bit error rate performance than existing detectors, including Viterbi with imperfect CSI and other neural network methods, particularly in rapidly changing channels.
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. In addition, we also evaluate the performance of this detection method applied to data sent over a molecular communication platform, where the channel model is difficult to model analytically. We show that SBRNN is computationally efficient, and can perform detection under various channel conditions without knowing the underlying channel model. We also demonstrate that the bit error rate (BER) performance of the proposed SBRNN detector is better than that of a Viterbi detector with imperfect CSI as well as that of other NN detectors that have been previously proposed. Finally, we show that the SBRNN can perform well in rapidly changing channels, where the coherence time is on the order of a single symbol duration.