Neural Network Detection of Data Sequences in Communication Systems

By Nariman Farsad, Andrea J. Goldsmith. 2018

318 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Molecular Biology

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Citations in the corpus

6G and Beyond: The Future of Wireless Communications Systems (2020, methodology): “in diffusion-based channels commonly found in molecular communication, the channel characteristics depend largely on the environment, making them challenging to model theoretically [77].”
DeepJSCC-f: Deep Joint Source-Channel Coding of Images With Feedback (2019, background): “More impressive results have been achieved through DNNbased designs in settings where current codes fall short of the fundamental theoretical limits; for example, for channels that are harder to model, such as optical [28] and molecular [29] communications, or in settings no practical coding scheme ”
Learning to Detect (2018, methodology): “This approach of decoding without CSI (channel state information) is further developed in [23].”
A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “may occur when operating over channels that do not have well-established mathematical models, such as for molecular communications [31].”
Model-Based Deep Learning (2020, methodology): “are compared to the performance of the model-based SP methods, which requires complete knowledge of the underlying statistical model, as well as the sliding bidirectional RNN detector proposed in [85] for such setups, which utilizes a conventional DNN architecture that does not explicitly account fo”
Massive MIMO Detection Techniques: A Survey (2019, background): “In [157], an investigation on how techniques from deep learning can be utilized to train a detection algorithm from samples of transmitted and received signals is conducted.”
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future (2020, methodology): “Using advanced AI/ML techniques offers benefits over traditional model-based approaches [251].”
6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications (2020, background): “Examples of application of machine learning tools to the physical layer of a communication system have been already studied, under unknown channels, using an autoencoder (AE) [58], [59], a recurrent neural network (RNN) [60], and a generative adversarial network (GAN) [61].”
Learning Optimal Resource Allocations in Wireless Systems (2018, background): “engineering as it is possible to construct a problem having a given solution [18], [19].”