Machine Learning-based Channel Prediction in Wideband Massive MIMO Systems with Small Overhead for Online Training Article Swipe
Beomsoo Ko
,
Hwanjin Kim
,
Minje Kim
,
Junil Choi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.12134
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.12134
Channel prediction compensates for outdated channel state information in multiple-input multiple-output (MIMO) systems. Machine learning (ML) techniques have recently been implemented to design channel predictors by leveraging the temporal correlation of wireless channels. However, most ML-based channel prediction techniques have only considered offline training when generating channel predictors, which can result in poor performance when encountering channel environments different from the ones they were trained on. To ensure prediction performance in varying channel conditions, we propose an online re-training framework that trains the channel predictor from scratch to effectively capture and respond to changes in the wireless environment. The training time includes data collection time and neural network training time, and should be minimized for practical channel predictors. To reduce the training time, especially data collection time, we propose a novel ML-based channel prediction technique called aggregated learning (AL) approach for wideband massive MIMO systems. In the proposed AL approach, the training data can be split and aggregated either in an array domain or frequency domain, which are the channel domains of MIMO-OFDM systems. This processing can significantly reduce the time for data collection. Our numerical results show that the AL approach even improves channel prediction performance in various scenarios with small training time overhead.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2408.12134
- https://arxiv.org/pdf/2408.12134
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405622307
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405622307Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2408.12134Digital Object Identifier
- Title
-
Machine Learning-based Channel Prediction in Wideband Massive MIMO Systems with Small Overhead for Online TrainingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-22Full publication date if available
- Authors
-
Beomsoo Ko, Hwanjin Kim, Minje Kim, Junil ChoiList of authors in order
- Landing page
-
https://arxiv.org/abs/2408.12134Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2408.12134Direct 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/2408.12134Direct OA link when available
- Concepts
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Overhead (engineering), MIMO, Training (meteorology), Computer science, Wideband, Channel (broadcasting), Computer network, Machine learning, Artificial intelligence, Computer engineering, Electronic engineering, Engineering, Geography, Operating system, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.especially | 123 |
| abstract_inverted_index.generating | 45 |
| abstract_inverted_index.leveraging | 26 |
| abstract_inverted_index.prediction | 1, 37, 68, 133, 195 |
| abstract_inverted_index.predictors | 24 |
| abstract_inverted_index.processing | 175 |
| abstract_inverted_index.techniques | 16, 38 |
| abstract_inverted_index.collection. | 183 |
| abstract_inverted_index.compensates | 2 |
| abstract_inverted_index.conditions, | 73 |
| abstract_inverted_index.correlation | 29 |
| abstract_inverted_index.effectively | 88 |
| abstract_inverted_index.implemented | 20 |
| abstract_inverted_index.information | 7 |
| abstract_inverted_index.performance | 53, 69, 196 |
| abstract_inverted_index.predictors, | 47 |
| abstract_inverted_index.predictors. | 117 |
| abstract_inverted_index.re-training | 78 |
| abstract_inverted_index.encountering | 55 |
| abstract_inverted_index.environment. | 97 |
| abstract_inverted_index.environments | 57 |
| abstract_inverted_index.significantly | 177 |
| abstract_inverted_index.multiple-input | 9 |
| abstract_inverted_index.multiple-output | 10 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |