Learning to Detect

In this paper, we consider multiple-input-multiple-output detection using deep neural networks. We introduce two different deep architectures: a standard fully connected multi-layer network, and a detection network (DetNet), which is specifically designed for the task. The structure of DetNet is obt...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 67; no. 10; pp. 2554 - 2564
Main Authors: Samuel, Neev, Diskin, Tzvi, Wiesel, Ami
Format: Journal Article
Language:English
Published: New York IEEE 15.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Complexity theory
Deep learning
Detectors
MIMO communication
MIMO detection
Multilayers
Neural networks
Signal processing algorithms
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:In this paper, we consider multiple-input-multiple-output detection using deep neural networks. We introduce two different deep architectures: a standard fully connected multi-layer network, and a detection network (DetNet), which is specifically designed for the task. The structure of DetNet is obtained by unfolding the iterations of a projected gradient descent algorithm into a network. We compare the accuracy and runtime complexity of the proposed approaches and achieve state-of-the-art performance while maintaining low computational requirements. Furthermore, we manage to train a single network to detect over an entire distribution of channels. Finally, we consider detection with soft outputs and show that the networks can easily be modified to produce soft decisions.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2019.2899805