Deep learning-aided high-precision data detection for massive MU-MIMO systems Article Swipe
Sen Yang
,
Zerun Li
,
Jinhui Wei
,
Zuocheng Xing
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1051/matecconf/202133604007
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1051/matecconf/202133604007
The data detector for future wireless system needs to achieve high throughput and low bit error rate (BER) with low computational complexity. In this paper, we propose a deep neural networks (DNNs) learning aided iterative detection algorithm. We first propose a convex optimization-based method for calculating the efficient detection of iterative soft output data, and then propose a method for adjusting the iteration parameters using the powerful data driven by DNNs, which achieves fast convergence and strong robustness. The results show that the proposed method can achieve the same performance as the known algorithm at a lower computation complexity cost.
Related Topics
Concepts
Computer science
Robustness (evolution)
MIMO
Computation
Detector
Computational complexity theory
Rate of convergence
Deep learning
Algorithm
Convex optimization
Bit error rate
Regular polygon
Artificial intelligence
Computer engineering
Channel (broadcasting)
Mathematics
Decoding methods
Biochemistry
Chemistry
Gene
Telecommunications
Geometry
Computer network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/matecconf/202133604007
- https://www.matec-conferences.org/articles/matecconf/pdf/2021/05/matecconf_cscns20_04007.pdf
- OA Status
- diamond
- References
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3130516875
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3130516875Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1051/matecconf/202133604007Digital Object Identifier
- Title
-
Deep learning-aided high-precision data detection for massive MU-MIMO systemsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Sen Yang, Zerun Li, Jinhui Wei, Zuocheng XingList of authors in order
- Landing page
-
https://doi.org/10.1051/matecconf/202133604007Publisher landing page
- PDF URL
-
https://www.matec-conferences.org/articles/matecconf/pdf/2021/05/matecconf_cscns20_04007.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.matec-conferences.org/articles/matecconf/pdf/2021/05/matecconf_cscns20_04007.pdfDirect OA link when available
- Concepts
-
Computer science, Robustness (evolution), MIMO, Computation, Detector, Computational complexity theory, Rate of convergence, Deep learning, Algorithm, Convex optimization, Bit error rate, Regular polygon, Artificial intelligence, Computer engineering, Channel (broadcasting), Mathematics, Decoding methods, Biochemistry, Chemistry, Gene, Telecommunications, Geometry, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
6Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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