Neural network detection of data sequences in communication systems

Nariman Farsad, Andrea Goldsmith

Research output: Contribution to journalArticlepeer-review

246 Scopus citations


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 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.

Original languageEnglish (US)
Article number8454325
Pages (from-to)5663-5678
Number of pages16
JournalIEEE Transactions on Signal Processing
Issue number21
StatePublished - Nov 1 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


  • Machine learning
  • communication systems
  • deep learning
  • detection
  • free-space optical communication
  • molecular communication
  • optical communication
  • supervised learning


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