期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 67, 期 10, 页码 2554-2564出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2019.2899805
关键词
MIMO detection; deep learning; neural networks
资金
- Heron Consortium
- ISF [1339/15]
In this paper, we consider multiple-input-multipleoutput 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 run-time 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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