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Neural Network Detection of Data Sequences in Communication Systems. (arXiv:1802.02046v1 [eess.SP])
来源于:arXiv
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 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 chemical communication systems. In addition, we also evaluate the
performance of this detection method applied to data sent over a chemical
communication platform, where the channel model is difficult to model
analytically. We show that SBRNN is computationally efficient, and can perform
detection unde 查看全文>>