DeepTx: Deep Learning Beamforming with Channel Prediction Article Swipe
Janne M. J. Huttunen
,
Dani Korpi
,
Mikko Honkala
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.07998
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.07998
Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance.
Related Topics
Concepts
Telecommunications link
Beamforming
Computer science
Convolutional neural network
Transmitter
WSDMA
Channel (broadcasting)
Deep learning
Artificial intelligence
Electronic engineering
Computer engineering
Machine learning
Real-time computing
MIMO
Precoding
Computer network
Engineering
Telecommunications
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2202.07998
- https://arxiv.org/pdf/2202.07998
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221145449
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4221145449Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2202.07998Digital Object Identifier
- Title
-
DeepTx: Deep Learning Beamforming with Channel PredictionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-16Full publication date if available
- Authors
-
Janne M. J. Huttunen, Dani Korpi, Mikko HonkalaList of authors in order
- Landing page
-
https://arxiv.org/abs/2202.07998Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2202.07998Direct 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/2202.07998Direct OA link when available
- Concepts
-
Telecommunications link, Beamforming, Computer science, Convolutional neural network, Transmitter, WSDMA, Channel (broadcasting), Deep learning, Artificial intelligence, Electronic engineering, Computer engineering, Machine learning, Real-time computing, MIMO, Precoding, Computer network, Engineering, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4221145449 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2202.07998 |
| ids.doi | https://doi.org/10.48550/arxiv.2202.07998 |
| ids.openalex | https://openalex.org/W4221145449 |
| fwci | |
| type | preprint |
| title | DeepTx: Deep Learning Beamforming with Channel Prediction |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11946 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.998199999332428 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2202 |
| topics[0].subfield.display_name | Aerospace Engineering |
| topics[0].display_name | Antenna Design and Optimization |
| topics[1].id | https://openalex.org/T10936 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9947999715805054 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Millimeter-Wave Propagation and Modeling |
| topics[2].id | https://openalex.org/T10069 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9930999875068665 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2202 |
| topics[2].subfield.display_name | Aerospace Engineering |
| topics[2].display_name | Antenna Design and Analysis |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C138660444 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8709224462509155 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q5607897 |
| concepts[0].display_name | Telecommunications link |
| concepts[1].id | https://openalex.org/C54197355 |
| concepts[1].level | 2 |
| concepts[1].score | 0.8661215305328369 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q5782992 |
| concepts[1].display_name | Beamforming |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.7555032968521118 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C81363708 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5653249621391296 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[3].display_name | Convolutional neural network |
| concepts[4].id | https://openalex.org/C47798520 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5572945475578308 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q190157 |
| concepts[4].display_name | Transmitter |
| concepts[5].id | https://openalex.org/C57466844 |
| concepts[5].level | 5 |
| concepts[5].score | 0.5494049787521362 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7955559 |
| concepts[5].display_name | WSDMA |
| concepts[6].id | https://openalex.org/C127162648 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5429524779319763 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q16858953 |
| concepts[6].display_name | Channel (broadcasting) |
| concepts[7].id | https://openalex.org/C108583219 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4445438086986542 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[7].display_name | Deep learning |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3743041157722473 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C24326235 |
| concepts[9].level | 1 |
| concepts[9].score | 0.34309321641921997 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q126095 |
| concepts[9].display_name | Electronic engineering |
| concepts[10].id | https://openalex.org/C113775141 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3410608768463135 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q428691 |
| concepts[10].display_name | Computer engineering |
| concepts[11].id | https://openalex.org/C119857082 |
| concepts[11].level | 1 |
| concepts[11].score | 0.32989686727523804 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[11].display_name | Machine learning |
| concepts[12].id | https://openalex.org/C79403827 |
| concepts[12].level | 1 |
| concepts[12].score | 0.32809579372406006 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[12].display_name | Real-time computing |
| concepts[13].id | https://openalex.org/C207987634 |
| concepts[13].level | 3 |
| concepts[13].score | 0.25861281156539917 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q176862 |
| concepts[13].display_name | MIMO |
| concepts[14].id | https://openalex.org/C160562895 |
| concepts[14].level | 4 |
| concepts[14].score | 0.2516956925392151 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7239557 |
| concepts[14].display_name | Precoding |
| concepts[15].id | https://openalex.org/C31258907 |
| concepts[15].level | 1 |
| concepts[15].score | 0.24695855379104614 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[15].display_name | Computer network |
| concepts[16].id | https://openalex.org/C127413603 |
| concepts[16].level | 0 |
| concepts[16].score | 0.21977347135543823 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[16].display_name | Engineering |
| concepts[17].id | https://openalex.org/C76155785 |
| concepts[17].level | 1 |
| concepts[17].score | 0.2030656933784485 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[17].display_name | Telecommunications |
| keywords[0].id | https://openalex.org/keywords/telecommunications-link |
| keywords[0].score | 0.8709224462509155 |
| keywords[0].display_name | Telecommunications link |
| keywords[1].id | https://openalex.org/keywords/beamforming |
| keywords[1].score | 0.8661215305328369 |
| keywords[1].display_name | Beamforming |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.7555032968521118 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[3].score | 0.5653249621391296 |
| keywords[3].display_name | Convolutional neural network |
| keywords[4].id | https://openalex.org/keywords/transmitter |
| keywords[4].score | 0.5572945475578308 |
| keywords[4].display_name | Transmitter |
| keywords[5].id | https://openalex.org/keywords/wsdma |
| keywords[5].score | 0.5494049787521362 |
| keywords[5].display_name | WSDMA |
| keywords[6].id | https://openalex.org/keywords/channel |
| keywords[6].score | 0.5429524779319763 |
| keywords[6].display_name | Channel (broadcasting) |
| keywords[7].id | https://openalex.org/keywords/deep-learning |
| keywords[7].score | 0.4445438086986542 |
| keywords[7].display_name | Deep learning |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.3743041157722473 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/electronic-engineering |
| keywords[9].score | 0.34309321641921997 |
| keywords[9].display_name | Electronic engineering |
| keywords[10].id | https://openalex.org/keywords/computer-engineering |
| keywords[10].score | 0.3410608768463135 |
| keywords[10].display_name | Computer engineering |
| keywords[11].id | https://openalex.org/keywords/machine-learning |
| keywords[11].score | 0.32989686727523804 |
| keywords[11].display_name | Machine learning |
| keywords[12].id | https://openalex.org/keywords/real-time-computing |
| keywords[12].score | 0.32809579372406006 |
| keywords[12].display_name | Real-time computing |
| keywords[13].id | https://openalex.org/keywords/mimo |
| keywords[13].score | 0.25861281156539917 |
| keywords[13].display_name | MIMO |
| keywords[14].id | https://openalex.org/keywords/precoding |
| keywords[14].score | 0.2516956925392151 |
| keywords[14].display_name | Precoding |
| keywords[15].id | https://openalex.org/keywords/computer-network |
| keywords[15].score | 0.24695855379104614 |
| keywords[15].display_name | Computer network |
| keywords[16].id | https://openalex.org/keywords/engineering |
| keywords[16].score | 0.21977347135543823 |
| keywords[16].display_name | Engineering |
| keywords[17].id | https://openalex.org/keywords/telecommunications |
| keywords[17].score | 0.2030656933784485 |
| keywords[17].display_name | Telecommunications |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2202.07998 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2202.07998 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2202.07998 |
| locations[1].id | doi:10.48550/arxiv.2202.07998 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2202.07998 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5065186178 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-4549-0272 |
| authorships[0].author.display_name | Janne M. J. Huttunen |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Huttunen, Janne M. J. |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5003942874 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3460-7436 |
| authorships[1].author.display_name | Dani Korpi |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Korpi, Dani |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5056119397 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5916-5908 |
| authorships[2].author.display_name | Mikko Honkala |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Honkala, Mikko |
| authorships[2].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2202.07998 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | DeepTx: Deep Learning Beamforming with Channel Prediction |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11946 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.998199999332428 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2202 |
| primary_topic.subfield.display_name | Aerospace Engineering |
| primary_topic.display_name | Antenna Design and Optimization |
| related_works | https://openalex.org/W2780252087, https://openalex.org/W2890273742, https://openalex.org/W2121063273, https://openalex.org/W2337309234, https://openalex.org/W2130902301, https://openalex.org/W4390447722, https://openalex.org/W2923631784, https://openalex.org/W1981344466, https://openalex.org/W2036968796, https://openalex.org/W2092456355 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2202.07998 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2202.07998 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2202.07998 |
| primary_location.id | pmh:oai:arXiv.org:2202.07998 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2202.07998 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2202.07998 |
| publication_date | 2022-02-16 |
| publication_year | 2022 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 23, 60, 64, 85, 95 |
| abstract_inverted_index.In | 41, 53 |
| abstract_inverted_index.UE | 102 |
| abstract_inverted_index.as | 69 |
| abstract_inverted_index.be | 76 |
| abstract_inverted_index.in | 10, 84, 133 |
| abstract_inverted_index.is | 82, 99, 112 |
| abstract_inverted_index.it | 35, 124 |
| abstract_inverted_index.of | 13, 22, 108 |
| abstract_inverted_index.on | 46, 101 |
| abstract_inverted_index.to | 36, 75, 113, 128 |
| abstract_inverted_index.we | 17, 44, 55 |
| abstract_inverted_index.CNN | 61, 81 |
| abstract_inverted_index.The | 80, 105, 142 |
| abstract_inverted_index.and | 33, 58, 91, 120, 131 |
| abstract_inverted_index.but | 123 |
| abstract_inverted_index.can | 125 |
| abstract_inverted_index.for | 7, 30, 50, 63, 78 |
| abstract_inverted_index.the | 11, 20, 51, 109, 115, 134, 138, 147 |
| abstract_inverted_index.use | 21 |
| abstract_inverted_index.also | 126 |
| abstract_inverted_index.been | 5 |
| abstract_inverted_index.both | 89 |
| abstract_inverted_index.deep | 24 |
| abstract_inverted_index.have | 3, 18 |
| abstract_inverted_index.loss | 96 |
| abstract_inverted_index.main | 106 |
| abstract_inverted_index.many | 8 |
| abstract_inverted_index.task | 107 |
| abstract_inverted_index.that | 98 |
| abstract_inverted_index.this | 42 |
| abstract_inverted_index.used | 77 |
| abstract_inverted_index.with | 94 |
| abstract_inverted_index.(CNN) | 29 |
| abstract_inverted_index.based | 100 |
| abstract_inverted_index.field | 12 |
| abstract_inverted_index.focus | 45 |
| abstract_inverted_index.fully | 25 |
| abstract_inverted_index.given | 65 |
| abstract_inverted_index.learn | 127 |
| abstract_inverted_index.shown | 34 |
| abstract_inverted_index.tasks | 9 |
| abstract_inverted_index.whole | 135 |
| abstract_inverted_index.actual | 139 |
| abstract_inverted_index.chain, | 136 |
| abstract_inverted_index.errors | 132 |
| abstract_inverted_index.gains. | 40 |
| abstract_inverted_index.handle | 129 |
| abstract_inverted_index.input, | 70 |
| abstract_inverted_index.manner | 87 |
| abstract_inverted_index.neural | 27, 110 |
| abstract_inverted_index.phase. | 141 |
| abstract_inverted_index.slots, | 122 |
| abstract_inverted_index.study, | 43 |
| abstract_inverted_index.uplink | 66, 90, 119 |
| abstract_inverted_index.which, | 62 |
| abstract_inverted_index.Machine | 0 |
| abstract_inverted_index.between | 118 |
| abstract_inverted_index.channel | 67, 73, 116 |
| abstract_inverted_index.machine | 47 |
| abstract_inverted_index.network | 28, 111 |
| abstract_inverted_index.outputs | 71 |
| abstract_inverted_index.predict | 114 |
| abstract_inverted_index.propose | 59 |
| abstract_inverted_index.provide | 37 |
| abstract_inverted_index.trained | 83 |
| abstract_inverted_index.consider | 56 |
| abstract_inverted_index.downlink | 72, 92, 121 |
| abstract_inverted_index.estimate | 68 |
| abstract_inverted_index.function | 97 |
| abstract_inverted_index.improved | 148 |
| abstract_inverted_index.learning | 1, 48 |
| abstract_inverted_index.proposed | 19 |
| abstract_inverted_index.provided | 143 |
| abstract_inverted_index.receiver | 31, 103 |
| abstract_inverted_index.recently | 4 |
| abstract_inverted_index.wireless | 14 |
| abstract_inverted_index.evolution | 117 |
| abstract_inverted_index.including | 137 |
| abstract_inverted_index.numerical | 144 |
| abstract_inverted_index.algorithms | 2, 49 |
| abstract_inverted_index.considered | 6 |
| abstract_inverted_index.processing | 32 |
| abstract_inverted_index.supervised | 86 |
| abstract_inverted_index.Previously, | 16 |
| abstract_inverted_index.beamforming | 57, 140, 149 |
| abstract_inverted_index.considering | 88 |
| abstract_inverted_index.demonstrate | 146 |
| abstract_inverted_index.experiments | 145 |
| abstract_inverted_index.information | 74 |
| abstract_inverted_index.particular, | 54 |
| abstract_inverted_index.performance | 39 |
| abstract_inverted_index.beamforming. | 79 |
| abstract_inverted_index.considerable | 38 |
| abstract_inverted_index.performance. | 104, 150 |
| abstract_inverted_index.transmitter. | 52 |
| abstract_inverted_index.convolutional | 26 |
| abstract_inverted_index.transmissions | 93 |
| abstract_inverted_index.inefficiencies | 130 |
| abstract_inverted_index.communications. | 15 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |