Supervised Contrastive CSI Representation Learning for Massive MIMO Positioning Article Swipe
Junquan Deng
,
Wei Shi
,
Jianzhao Zhang
,
Xianyu Zhang
,
Chuan Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.12796
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.12796
Similarity metric is crucial for massive MIMO positioning utilizing channel state information~(CSI). In this letter, we propose a novel massive MIMO CSI similarity learning method via deep convolutional neural network~(DCNN) and contrastive learning. A contrastive loss function is designed considering multiple positive and negative CSI samples drawn from a training dataset. The DCNN encoder is trained using the loss so that positive samples are mapped to points close to the anchor's encoding, while encodings of negative samples are kept away from the anchor's in the representation space. Evaluation results of fingerprint-based positioning on a real-world CSI dataset show that the learned similarity metric improves positioning accuracy significantly compared with other known state-of-the-art methods.
Related Topics
Concepts
Similarity (geometry)
Encoder
Representation (politics)
Artificial intelligence
Computer science
Metric (unit)
Convolutional neural network
Channel state information
Encoding (memory)
MIMO
Deep learning
Autoencoder
Pattern recognition (psychology)
Feature learning
Channel (broadcasting)
Image (mathematics)
Telecommunications
Engineering
Operating system
Political science
Politics
Wireless
Operations management
Law
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2204.12796
- https://arxiv.org/pdf/2204.12796
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309417027
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4309417027Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2204.12796Digital Object Identifier
- Title
-
Supervised Contrastive CSI Representation Learning for Massive MIMO PositioningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-27Full publication date if available
- Authors
-
Junquan Deng, Wei Shi, Jianzhao Zhang, Xianyu Zhang, Chuan ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2204.12796Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2204.12796Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2204.12796Direct OA link when available
- Concepts
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Similarity (geometry), Encoder, Representation (politics), Artificial intelligence, Computer science, Metric (unit), Convolutional neural network, Channel state information, Encoding (memory), MIMO, Deep learning, Autoencoder, Pattern recognition (psychology), Feature learning, Channel (broadcasting), Image (mathematics), Telecommunications, Engineering, Operating system, Political science, Politics, Wireless, Operations management, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.from | 47, 80 |
| abstract_inverted_index.kept | 78 |
| abstract_inverted_index.loss | 35, 58 |
| abstract_inverted_index.show | 97 |
| abstract_inverted_index.that | 60, 98 |
| abstract_inverted_index.this | 13 |
| abstract_inverted_index.with | 108 |
| abstract_inverted_index.close | 67 |
| abstract_inverted_index.drawn | 46 |
| abstract_inverted_index.known | 110 |
| abstract_inverted_index.novel | 18 |
| abstract_inverted_index.other | 109 |
| abstract_inverted_index.state | 10 |
| abstract_inverted_index.using | 56 |
| abstract_inverted_index.while | 72 |
| abstract_inverted_index.mapped | 64 |
| abstract_inverted_index.method | 24 |
| abstract_inverted_index.metric | 1, 102 |
| abstract_inverted_index.neural | 28 |
| abstract_inverted_index.points | 66 |
| abstract_inverted_index.space. | 86 |
| abstract_inverted_index.channel | 9 |
| abstract_inverted_index.crucial | 3 |
| abstract_inverted_index.dataset | 96 |
| abstract_inverted_index.encoder | 53 |
| abstract_inverted_index.learned | 100 |
| abstract_inverted_index.letter, | 14 |
| abstract_inverted_index.massive | 5, 19 |
| abstract_inverted_index.propose | 16 |
| abstract_inverted_index.results | 88 |
| abstract_inverted_index.samples | 45, 62, 76 |
| abstract_inverted_index.trained | 55 |
| abstract_inverted_index.accuracy | 105 |
| abstract_inverted_index.anchor's | 70, 82 |
| abstract_inverted_index.compared | 107 |
| abstract_inverted_index.dataset. | 50 |
| abstract_inverted_index.designed | 38 |
| abstract_inverted_index.function | 36 |
| abstract_inverted_index.improves | 103 |
| abstract_inverted_index.learning | 23 |
| abstract_inverted_index.methods. | 112 |
| abstract_inverted_index.multiple | 40 |
| abstract_inverted_index.negative | 43, 75 |
| abstract_inverted_index.positive | 41, 61 |
| abstract_inverted_index.training | 49 |
| abstract_inverted_index.encoding, | 71 |
| abstract_inverted_index.encodings | 73 |
| abstract_inverted_index.learning. | 32 |
| abstract_inverted_index.utilizing | 8 |
| abstract_inverted_index.Evaluation | 87 |
| abstract_inverted_index.Similarity | 0 |
| abstract_inverted_index.real-world | 94 |
| abstract_inverted_index.similarity | 22, 101 |
| abstract_inverted_index.considering | 39 |
| abstract_inverted_index.contrastive | 31, 34 |
| abstract_inverted_index.positioning | 7, 91, 104 |
| abstract_inverted_index.convolutional | 27 |
| abstract_inverted_index.significantly | 106 |
| abstract_inverted_index.network~(DCNN) | 29 |
| abstract_inverted_index.representation | 85 |
| abstract_inverted_index.state-of-the-art | 111 |
| abstract_inverted_index.fingerprint-based | 90 |
| abstract_inverted_index.information~(CSI). | 11 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |