The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Model-Driven Deep Learning for MIMO Detection
Open accessAbstract
In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO detector can be rapidly trained with a much smaller data set. The proposed MIMO detector can be extended to soft-input soft-output detection easily. Furthermore, we investigate joint MIMO channel estimation and signal detection (JCESD), where the detector takes channel estimation error and channel statistics into consideration while channel estimation is refined by detected data and considers the detection error. Based on numerical results, the model-driven DL based MIMO detector significantly improves the performance of corresponding traditional iterative detector, outperforms other DL-based MIMO detectors and exhibits superior robustness to various mismatches.
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Cites
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Self-citationLearning Fast Approximations of Sparse Coding
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Deep Learning for Massive MIMO CSI Feedback
Wen, C.-K., Shih, W.-T., & Jin, S. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2018.2818160 · Cited by 1,174
Self-citationQuaDRiGa: A 3-D Multi-Cell Channel Model With Time Evolution for Enabling Virtual Field Trials
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Metadata
- Authors
- He, H., Wen, C.-K., Jin, S., & Li, G. Y.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2020
- Volume
- 68
- Pages
- 1702-1715
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W3008026851
- Cited by
- 454
- References
- 42
- FWCI
- 20.76
- Type
- journal article
- Language
- en
- Open access
- green
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