The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Deep MIMO detection
Open accessAbstract
In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and propose a modern neural network architecture suitable for this detection task. First, we consider the case in which the MIMO channel is constant, and we learn a detector for a specific system. Next, we consider the harder case in which the parameters are known yet changing and a single detector must be learned for all multiple varying channels. We demonstrate the performance of our deep MIMO detector using numerical simulations in comparison to competing methods including approximate message passing and semidefinite relaxation. The results show that deep networks can achieve state of the art accuracy with significantly lower complexity while providing robustness against ill conditioned channels and mis-specified noise variance.
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Cites
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Cited by
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
Deep Learning in Mobile and Wireless Networking: A Survey
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Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
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Deep Learning Based Communication Over the Air
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Learning to Optimize: Training Deep Neural Networks for Interference Management
Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2866382 · Cited by 874
Metadata
- Authors
- Samuel, N., Diskin, T., & Wiesel, A.
- Venue
- 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1-5
- OpenAlex
- W2623410759
- Cited by
- 429
- References
- 26
- FWCI
- 81.72
- Type
- conference paper
- Language
- en
- Open access
- green
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