Context-Tree-Based Lossy Compression and Its Application to CSI Representation Article Swipe
Henrique K. Miyamoto
,
Sheng Yang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tcomm.2022.3173002
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tcomm.2022.3173002
We propose novel compression algorithms for time-varying channel state\ninformation (CSI) in wireless communications. The proposed scheme combines\n(lossy) vector quantisation and (lossless) compression. First, the new vector\nquantisation technique is based on a class of parametrised companders applied\non each component of the normalised CSI vector. Our algorithm chooses a\nsuitable compander in an intuitively simple way whenever empirical data are\navailable. Then, the sequences of quantisation indices are compressed using a\ncontext-tree-based approach. Essentially, we update the estimate of the\nconditional distribution of the source at each instant and encode the current\nsymbol with the estimated distribution. The algorithms have low complexity, are\nlinear-time in both the spatial dimension and time duration, and can be\nimplemented in an online fashion. We run simulations to demonstrate the\neffectiveness of the proposed algorithms in such scenarios.\n
Related Topics
Concepts
Lossy compression
Lossless compression
Algorithm
Data compression
Computer science
Context (archaeology)
Tree (set theory)
ENCODE
Dimension (graph theory)
Theoretical computer science
Mathematics
Artificial intelligence
Biochemistry
Pure mathematics
Biology
Chemistry
Paleontology
Gene
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tcomm.2022.3173002
- OA Status
- green
- Cited By
- 4
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3209475909
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3209475909Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tcomm.2022.3173002Digital Object Identifier
- Title
-
Context-Tree-Based Lossy Compression and Its Application to CSI RepresentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-05Full publication date if available
- Authors
-
Henrique K. Miyamoto, Sheng YangList of authors in order
- Landing page
-
https://doi.org/10.1109/tcomm.2022.3173002Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2110.14748Direct OA link when available
- Concepts
-
Lossy compression, Lossless compression, Algorithm, Data compression, Computer science, Context (archaeology), Tree (set theory), ENCODE, Dimension (graph theory), Theoretical computer science, Mathematics, Artificial intelligence, Biochemistry, Pure mathematics, Biology, Chemistry, Paleontology, Gene, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3209475909 |
|---|---|
| doi | https://doi.org/10.1109/tcomm.2022.3173002 |
| ids.doi | https://doi.org/10.1109/tcomm.2022.3173002 |
| ids.mag | 3209475909 |
| ids.openalex | https://openalex.org/W3209475909 |
| fwci | 0.78319528 |
| type | article |
| title | Context-Tree-Based Lossy Compression and Its Application to CSI Representation |
| biblio.issue | 7 |
| biblio.volume | 70 |
| biblio.last_page | 4428 |
| biblio.first_page | 4417 |
| topics[0].id | https://openalex.org/T11269 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Algorithms and Data Compression |
| topics[1].id | https://openalex.org/T11321 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9988999962806702 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1705 |
| topics[1].subfield.display_name | Computer Networks and Communications |
| topics[1].display_name | Error Correcting Code Techniques |
| topics[2].id | https://openalex.org/T10901 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9980999827384949 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Advanced Data Compression Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C165021410 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7843558192253113 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q55564 |
| concepts[0].display_name | Lossy compression |
| concepts[1].id | https://openalex.org/C81081738 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7759538888931274 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q55542 |
| concepts[1].display_name | Lossless compression |
| concepts[2].id | https://openalex.org/C11413529 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6238003969192505 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[2].display_name | Algorithm |
| concepts[3].id | https://openalex.org/C78548338 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5982376933097839 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2493 |
| concepts[3].display_name | Data compression |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.5851840376853943 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C2779343474 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4769517481327057 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q3109175 |
| concepts[5].display_name | Context (archaeology) |
| concepts[6].id | https://openalex.org/C113174947 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4530482292175293 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2859736 |
| concepts[6].display_name | Tree (set theory) |
| concepts[7].id | https://openalex.org/C66746571 |
| concepts[7].level | 3 |
| concepts[7].score | 0.43014150857925415 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1134833 |
| concepts[7].display_name | ENCODE |
| concepts[8].id | https://openalex.org/C33676613 |
| concepts[8].level | 2 |
| concepts[8].score | 0.41240188479423523 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q13415176 |
| concepts[8].display_name | Dimension (graph theory) |
| concepts[9].id | https://openalex.org/C80444323 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3741037845611572 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[9].display_name | Theoretical computer science |
| concepts[10].id | https://openalex.org/C33923547 |
| concepts[10].level | 0 |
| concepts[10].score | 0.322549968957901 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[10].display_name | Mathematics |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.19441130757331848 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C55493867 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[12].display_name | Biochemistry |
| concepts[13].id | https://openalex.org/C202444582 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q837863 |
| concepts[13].display_name | Pure mathematics |
| concepts[14].id | https://openalex.org/C86803240 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[14].display_name | Biology |
| concepts[15].id | https://openalex.org/C185592680 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[15].display_name | Chemistry |
| concepts[16].id | https://openalex.org/C151730666 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[16].display_name | Paleontology |
| concepts[17].id | https://openalex.org/C104317684 |
| concepts[17].level | 2 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[17].display_name | Gene |
| concepts[18].id | https://openalex.org/C134306372 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[18].display_name | Mathematical analysis |
| keywords[0].id | https://openalex.org/keywords/lossy-compression |
| keywords[0].score | 0.7843558192253113 |
| keywords[0].display_name | Lossy compression |
| keywords[1].id | https://openalex.org/keywords/lossless-compression |
| keywords[1].score | 0.7759538888931274 |
| keywords[1].display_name | Lossless compression |
| keywords[2].id | https://openalex.org/keywords/algorithm |
| keywords[2].score | 0.6238003969192505 |
| keywords[2].display_name | Algorithm |
| keywords[3].id | https://openalex.org/keywords/data-compression |
| keywords[3].score | 0.5982376933097839 |
| keywords[3].display_name | Data compression |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.5851840376853943 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/context |
| keywords[5].score | 0.4769517481327057 |
| keywords[5].display_name | Context (archaeology) |
| keywords[6].id | https://openalex.org/keywords/tree |
| keywords[6].score | 0.4530482292175293 |
| keywords[6].display_name | Tree (set theory) |
| keywords[7].id | https://openalex.org/keywords/encode |
| keywords[7].score | 0.43014150857925415 |
| keywords[7].display_name | ENCODE |
| keywords[8].id | https://openalex.org/keywords/dimension |
| keywords[8].score | 0.41240188479423523 |
| keywords[8].display_name | Dimension (graph theory) |
| keywords[9].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[9].score | 0.3741037845611572 |
| keywords[9].display_name | Theoretical computer science |
| keywords[10].id | https://openalex.org/keywords/mathematics |
| keywords[10].score | 0.322549968957901 |
| keywords[10].display_name | Mathematics |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.19441130757331848 |
| keywords[11].display_name | Artificial intelligence |
| language | en |
| locations[0].id | doi:10.1109/tcomm.2022.3173002 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S196647941 |
| locations[0].source.issn | 0090-6778, 1558-0857 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0090-6778 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | IEEE Transactions on Communications |
| locations[0].source.host_organization | https://openalex.org/P4310316002 |
| locations[0].source.host_organization_name | IEEE Communications Society |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310316002, https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | IEEE Communications Society, Institute of Electrical and Electronics Engineers |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IEEE Transactions on Communications |
| locations[0].landing_page_url | https://doi.org/10.1109/tcomm.2022.3173002 |
| locations[1].id | pmh:oai:arXiv.org:2110.14748 |
| 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 | https://arxiv.org/pdf/2110.14748 |
| locations[1].version | submittedVersion |
| locations[1].raw_type | text |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://arxiv.org/abs/2110.14748 |
| locations[2].id | pmh:oai:HAL:hal-03676199v1 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306402512 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | HAL (Le Centre pour la Communication Scientifique Directe) |
| locations[2].source.host_organization | https://openalex.org/I1294671590 |
| locations[2].source.host_organization_name | Centre National de la Recherche Scientifique |
| locations[2].source.host_organization_lineage | https://openalex.org/I1294671590 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | info:eu-repo/semantics/article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | ISSN: 0090-6778 |
| locations[2].landing_page_url | https://hal.science/hal-03676199 |
| locations[3].id | pmh:oai:HAL:hal-04852763v1 |
| locations[3].is_oa | False |
| locations[3].source.id | https://openalex.org/S4306402512 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | HAL (Le Centre pour la Communication Scientifique Directe) |
| locations[3].source.host_organization | https://openalex.org/I1294671590 |
| locations[3].source.host_organization_name | Centre National de la Recherche Scientifique |
| locations[3].source.host_organization_lineage | https://openalex.org/I1294671590 |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | info:eu-repo/semantics/article |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | ISSN: 0090-6778 |
| locations[3].landing_page_url | https://hal.science/hal-04852763 |
| indexed_in | arxiv, crossref |
| authorships[0].author.id | https://openalex.org/A5010865156 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1131-2790 |
| authorships[0].author.display_name | Henrique K. Miyamoto |
| authorships[0].countries | BR, FR |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I181391015 |
| authorships[0].affiliations[0].raw_affiliation_string | Universidade Estadual de Campinas = University of Campinas |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I4210097418 |
| authorships[0].affiliations[1].raw_affiliation_string | Laboratoire des signaux et systèmes |
| authorships[0].institutions[0].id | https://openalex.org/I181391015 |
| authorships[0].institutions[0].ror | https://ror.org/04wffgt70 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I181391015 |
| authorships[0].institutions[0].country_code | BR |
| authorships[0].institutions[0].display_name | Universidade Estadual de Campinas (UNICAMP) |
| authorships[0].institutions[1].id | https://openalex.org/I4210097418 |
| authorships[0].institutions[1].ror | https://ror.org/00skw9v43 |
| authorships[0].institutions[1].type | facility |
| authorships[0].institutions[1].lineage | https://openalex.org/I1294671590, https://openalex.org/I277688954, https://openalex.org/I277688954, https://openalex.org/I4210097418, https://openalex.org/I4210107720 |
| authorships[0].institutions[1].country_code | FR |
| authorships[0].institutions[1].display_name | Laboratoire des signaux et systèmes |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Henrique Miyamoto |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Laboratoire des signaux et systèmes, Universidade Estadual de Campinas = University of Campinas |
| authorships[1].author.id | https://openalex.org/A5087891218 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0643-0445 |
| authorships[1].author.display_name | Sheng Yang |
| authorships[1].countries | FR |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210097418 |
| authorships[1].affiliations[0].raw_affiliation_string | Laboratoire des signaux et systèmes |
| authorships[1].institutions[0].id | https://openalex.org/I4210097418 |
| authorships[1].institutions[0].ror | https://ror.org/00skw9v43 |
| authorships[1].institutions[0].type | facility |
| authorships[1].institutions[0].lineage | https://openalex.org/I1294671590, https://openalex.org/I277688954, https://openalex.org/I277688954, https://openalex.org/I4210097418, https://openalex.org/I4210107720 |
| authorships[1].institutions[0].country_code | FR |
| authorships[1].institutions[0].display_name | Laboratoire des signaux et systèmes |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Sheng Yang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Laboratoire des signaux et systèmes |
| 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/2110.14748 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Context-Tree-Based Lossy Compression and Its Application to CSI Representation |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11269 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Algorithms and Data Compression |
| related_works | https://openalex.org/W2385628723, https://openalex.org/W2547124190, https://openalex.org/W3180760233, https://openalex.org/W3035703949, https://openalex.org/W4247601675, https://openalex.org/W1970394887, https://openalex.org/W755971114, https://openalex.org/W2118338613, https://openalex.org/W1982468865, https://openalex.org/W4210455546 |
| cited_by_count | 4 |
| counts_by_year[0].year | 2023 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2022 |
| counts_by_year[1].cited_by_count | 3 |
| locations_count | 4 |
| best_oa_location.id | pmh:oai:arXiv.org:2110.14748 |
| 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/2110.14748 |
| 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/2110.14748 |
| primary_location.id | doi:10.1109/tcomm.2022.3173002 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S196647941 |
| primary_location.source.issn | 0090-6778, 1558-0857 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0090-6778 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | IEEE Transactions on Communications |
| primary_location.source.host_organization | https://openalex.org/P4310316002 |
| primary_location.source.host_organization_name | IEEE Communications Society |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310316002, https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | IEEE Communications Society, Institute of Electrical and Electronics Engineers |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Transactions on Communications |
| primary_location.landing_page_url | https://doi.org/10.1109/tcomm.2022.3173002 |
| publication_date | 2022-05-05 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W6872234227, https://openalex.org/W2116141601, https://openalex.org/W2963145597, https://openalex.org/W3003689553, https://openalex.org/W3112756300, https://openalex.org/W2962837755, https://openalex.org/W3120529131, https://openalex.org/W2884857433, https://openalex.org/W2099111195, https://openalex.org/W4293404332, https://openalex.org/W2107745473, https://openalex.org/W2122962290, https://openalex.org/W2163294786, https://openalex.org/W6602143575, https://openalex.org/W2103960658, https://openalex.org/W6685333077, https://openalex.org/W6752446581, https://openalex.org/W6752281925, https://openalex.org/W2884624555, https://openalex.org/W3045608583, https://openalex.org/W2129652681, https://openalex.org/W2072210981, https://openalex.org/W1634005169, https://openalex.org/W1825189686, https://openalex.org/W6679338740, https://openalex.org/W2001968606, https://openalex.org/W2157760979, https://openalex.org/W4287703689, https://openalex.org/W4403146937, https://openalex.org/W2809627566, https://openalex.org/W3112681748, https://openalex.org/W2478708596, https://openalex.org/W2172293472, https://openalex.org/W3104314953, https://openalex.org/W2130458416, https://openalex.org/W2808754986, https://openalex.org/W2055309977, https://openalex.org/W54859524 |
| referenced_works_count | 38 |
| abstract_inverted_index.a | 30 |
| abstract_inverted_index.We | 0, 111 |
| abstract_inverted_index.an | 49, 108 |
| abstract_inverted_index.at | 79 |
| abstract_inverted_index.in | 10, 48, 96, 107, 121 |
| abstract_inverted_index.is | 27 |
| abstract_inverted_index.of | 32, 38, 60, 73, 76, 117 |
| abstract_inverted_index.on | 29 |
| abstract_inverted_index.to | 114 |
| abstract_inverted_index.we | 69 |
| abstract_inverted_index.CSI | 41 |
| abstract_inverted_index.Our | 43 |
| abstract_inverted_index.The | 13, 90 |
| abstract_inverted_index.and | 19, 82, 101, 104 |
| abstract_inverted_index.are | 63 |
| abstract_inverted_index.can | 105 |
| abstract_inverted_index.for | 5 |
| abstract_inverted_index.low | 93 |
| abstract_inverted_index.new | 24 |
| abstract_inverted_index.run | 112 |
| abstract_inverted_index.the | 23, 39, 58, 71, 77, 84, 87, 98, 118 |
| abstract_inverted_index.way | 52 |
| abstract_inverted_index.both | 97 |
| abstract_inverted_index.data | 55 |
| abstract_inverted_index.each | 36, 80 |
| abstract_inverted_index.have | 92 |
| abstract_inverted_index.such | 122 |
| abstract_inverted_index.time | 102 |
| abstract_inverted_index.with | 86 |
| abstract_inverted_index.(CSI) | 9 |
| abstract_inverted_index.Then, | 57 |
| abstract_inverted_index.based | 28 |
| abstract_inverted_index.class | 31 |
| abstract_inverted_index.novel | 2 |
| abstract_inverted_index.using | 65 |
| abstract_inverted_index.First, | 22 |
| abstract_inverted_index.encode | 83 |
| abstract_inverted_index.online | 109 |
| abstract_inverted_index.scheme | 15 |
| abstract_inverted_index.simple | 51 |
| abstract_inverted_index.source | 78 |
| abstract_inverted_index.update | 70 |
| abstract_inverted_index.vector | 17 |
| abstract_inverted_index.channel | 7 |
| abstract_inverted_index.chooses | 45 |
| abstract_inverted_index.indices | 62 |
| abstract_inverted_index.instant | 81 |
| abstract_inverted_index.propose | 1 |
| abstract_inverted_index.spatial | 99 |
| abstract_inverted_index.vector. | 42 |
| abstract_inverted_index.estimate | 72 |
| abstract_inverted_index.fashion. | 110 |
| abstract_inverted_index.proposed | 14, 119 |
| abstract_inverted_index.whenever | 53 |
| abstract_inverted_index.wireless | 11 |
| abstract_inverted_index.algorithm | 44 |
| abstract_inverted_index.approach. | 67 |
| abstract_inverted_index.compander | 47 |
| abstract_inverted_index.component | 37 |
| abstract_inverted_index.dimension | 100 |
| abstract_inverted_index.duration, | 103 |
| abstract_inverted_index.empirical | 54 |
| abstract_inverted_index.estimated | 88 |
| abstract_inverted_index.sequences | 59 |
| abstract_inverted_index.technique | 26 |
| abstract_inverted_index.(lossless) | 20 |
| abstract_inverted_index.algorithms | 4, 91, 120 |
| abstract_inverted_index.companders | 34 |
| abstract_inverted_index.compressed | 64 |
| abstract_inverted_index.normalised | 40 |
| abstract_inverted_index.a\nsuitable | 46 |
| abstract_inverted_index.applied\non | 35 |
| abstract_inverted_index.complexity, | 94 |
| abstract_inverted_index.compression | 3 |
| abstract_inverted_index.demonstrate | 115 |
| abstract_inverted_index.intuitively | 50 |
| abstract_inverted_index.simulations | 113 |
| abstract_inverted_index.Essentially, | 68 |
| abstract_inverted_index.compression. | 21 |
| abstract_inverted_index.distribution | 75 |
| abstract_inverted_index.parametrised | 33 |
| abstract_inverted_index.quantisation | 18, 61 |
| abstract_inverted_index.scenarios.\n | 123 |
| abstract_inverted_index.time-varying | 6 |
| abstract_inverted_index.distribution. | 89 |
| abstract_inverted_index.are\navailable. | 56 |
| abstract_inverted_index.be\nimplemented | 106 |
| abstract_inverted_index.communications. | 12 |
| abstract_inverted_index.current\nsymbol | 85 |
| abstract_inverted_index.are\nlinear-time | 95 |
| abstract_inverted_index.the\nconditional | 74 |
| abstract_inverted_index.combines\n(lossy) | 16 |
| abstract_inverted_index.state\ninformation | 8 |
| abstract_inverted_index.the\neffectiveness | 116 |
| abstract_inverted_index.vector\nquantisation | 25 |
| abstract_inverted_index.a\ncontext-tree-based | 66 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.70654316 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |