SenseNet: Deep Learning based Wideband spectrum sensing and modulation classification network. Article Swipe
Next generation networks are expected to operate in licensed, shared as well as unlicensed spectrum to support spectrum demands of a wide variety of services.Due to shortage of radio spectrum, the need for communication systems(like cognitive radio) that can sense wideband spectrum and locate desired spectrum resources in real time has increased.Automatic modulation classifier (AMC) is an important part of wideband spectrum sensing (WSS) as it enables identification of incumbent users transmitting in the adjacent vacant spectrum.Most of the proposed AMC work on Nyquist samples which need to be further processed before they can be fed to the classifier.Working with Nyquist sampled signal demands high rate ADC and results in high power consumption and high sensing time which is unacceptable for next generation communication systems.To overcome this drawback we propose to use sub-nyquist sample based WSS and modulation classification. In this paper, we propose a novel architecture called SenseNet which combines the task of spectrum sensing and modulation classification into a single unified pipeline.The proposed method is endowed with the capability to perform blind WSS and modulation classification directly on raw sub-nyquist samples which reduces complexity and sensing time since no prior estimation of sparsity is required. We extensively compare the performance of our proposed method on WSS as well as modulation classification tasks for a wide range of modulation schemes, input datasets, and channel conditions.A significant drawback of using sub-nyquist samples is reduced performance compared to systems that employ nyquist sampled signal.However,we show that for the proposed method,the classification accuracy approaches to Nyquist sampling based deep learning AMC with an increase in signal to noise ratio.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://arxiv.org/pdf/1912.05255.pdf
- OA Status
- green
- Cited By
- 1
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2996565376
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2996565376Canonical identifier for this work in OpenAlex
- Title
-
SenseNet: Deep Learning based Wideband spectrum sensing and modulation classification network.Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-11Full publication date if available
- Authors
-
Shivam Chandhok, Himani Joshi, A V Subramanyam, Sumit J. DarakList of authors in order
- Landing page
-
https://arxiv.org/pdf/1912.05255.pdfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1912.05255.pdfDirect OA link when available
- Concepts
-
Cognitive radio, Wideband, Computer science, Nyquist–Shannon sampling theorem, Radio spectrum, Spectrum management, Modulation (music), Bandwidth (computing), Electronic engineering, Software-defined radio, Artificial intelligence, Wireless, Telecommunications, Engineering, Aesthetics, Philosophy, Computer visionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 1Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2996565376 |
|---|---|
| doi | |
| ids.mag | 2996565376 |
| ids.openalex | https://openalex.org/W2996565376 |
| fwci | 0.15361775 |
| type | article |
| title | SenseNet: Deep Learning based Wideband spectrum sensing and modulation classification network. |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12131 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9984999895095825 |
| 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 | Wireless Signal Modulation Classification |
| topics[1].id | https://openalex.org/T10662 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9319000244140625 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2211 |
| topics[1].subfield.display_name | Mechanics of Materials |
| topics[1].display_name | Ultrasonics and Acoustic Wave Propagation |
| topics[2].id | https://openalex.org/T12300 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9186000227928162 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2208 |
| topics[2].subfield.display_name | Electrical and Electronic Engineering |
| topics[2].display_name | Advanced Electrical Measurement Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C149946192 |
| concepts[0].level | 3 |
| concepts[0].score | 0.7984219789505005 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q3235733 |
| concepts[0].display_name | Cognitive radio |
| concepts[1].id | https://openalex.org/C2780202535 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7487069964408875 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q4524457 |
| concepts[1].display_name | Wideband |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.7258435487747192 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C288623 |
| concepts[3].level | 2 |
| concepts[3].score | 0.7015808820724487 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q679800 |
| concepts[3].display_name | Nyquist–Shannon sampling theorem |
| concepts[4].id | https://openalex.org/C92545706 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4893903136253357 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q902174 |
| concepts[4].display_name | Radio spectrum |
| concepts[5].id | https://openalex.org/C63029442 |
| concepts[5].level | 4 |
| concepts[5].score | 0.45907163619995117 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q6504978 |
| concepts[5].display_name | Spectrum management |
| concepts[6].id | https://openalex.org/C123079801 |
| concepts[6].level | 2 |
| concepts[6].score | 0.44793805480003357 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q750240 |
| concepts[6].display_name | Modulation (music) |
| concepts[7].id | https://openalex.org/C2776257435 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4295971393585205 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1576430 |
| concepts[7].display_name | Bandwidth (computing) |
| concepts[8].id | https://openalex.org/C24326235 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4291859269142151 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q126095 |
| concepts[8].display_name | Electronic engineering |
| concepts[9].id | https://openalex.org/C171115542 |
| concepts[9].level | 2 |
| concepts[9].score | 0.412455290555954 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1331892 |
| concepts[9].display_name | Software-defined radio |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.35863620042800903 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C555944384 |
| concepts[11].level | 2 |
| concepts[11].score | 0.26322710514068604 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q249 |
| concepts[11].display_name | Wireless |
| concepts[12].id | https://openalex.org/C76155785 |
| concepts[12].level | 1 |
| concepts[12].score | 0.24929463863372803 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[12].display_name | Telecommunications |
| concepts[13].id | https://openalex.org/C127413603 |
| concepts[13].level | 0 |
| concepts[13].score | 0.154097318649292 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[13].display_name | Engineering |
| concepts[14].id | https://openalex.org/C107038049 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q35986 |
| concepts[14].display_name | Aesthetics |
| concepts[15].id | https://openalex.org/C138885662 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[15].display_name | Philosophy |
| concepts[16].id | https://openalex.org/C31972630 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[16].display_name | Computer vision |
| keywords[0].id | https://openalex.org/keywords/cognitive-radio |
| keywords[0].score | 0.7984219789505005 |
| keywords[0].display_name | Cognitive radio |
| keywords[1].id | https://openalex.org/keywords/wideband |
| keywords[1].score | 0.7487069964408875 |
| keywords[1].display_name | Wideband |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.7258435487747192 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/nyquist–shannon-sampling-theorem |
| keywords[3].score | 0.7015808820724487 |
| keywords[3].display_name | Nyquist–Shannon sampling theorem |
| keywords[4].id | https://openalex.org/keywords/radio-spectrum |
| keywords[4].score | 0.4893903136253357 |
| keywords[4].display_name | Radio spectrum |
| keywords[5].id | https://openalex.org/keywords/spectrum-management |
| keywords[5].score | 0.45907163619995117 |
| keywords[5].display_name | Spectrum management |
| keywords[6].id | https://openalex.org/keywords/modulation |
| keywords[6].score | 0.44793805480003357 |
| keywords[6].display_name | Modulation (music) |
| keywords[7].id | https://openalex.org/keywords/bandwidth |
| keywords[7].score | 0.4295971393585205 |
| keywords[7].display_name | Bandwidth (computing) |
| keywords[8].id | https://openalex.org/keywords/electronic-engineering |
| keywords[8].score | 0.4291859269142151 |
| keywords[8].display_name | Electronic engineering |
| keywords[9].id | https://openalex.org/keywords/software-defined-radio |
| keywords[9].score | 0.412455290555954 |
| keywords[9].display_name | Software-defined radio |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.35863620042800903 |
| keywords[10].display_name | Artificial intelligence |
| keywords[11].id | https://openalex.org/keywords/wireless |
| keywords[11].score | 0.26322710514068604 |
| keywords[11].display_name | Wireless |
| keywords[12].id | https://openalex.org/keywords/telecommunications |
| keywords[12].score | 0.24929463863372803 |
| keywords[12].display_name | Telecommunications |
| keywords[13].id | https://openalex.org/keywords/engineering |
| keywords[13].score | 0.154097318649292 |
| keywords[13].display_name | Engineering |
| language | en |
| locations[0].id | mag:2996565376 |
| 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 | |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | arXiv (Cornell University) |
| locations[0].landing_page_url | https://arxiv.org/pdf/1912.05255.pdf |
| authorships[0].author.id | https://openalex.org/A5013255070 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Shivam Chandhok |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Shivam Chandhok |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5101941929 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0636-9509 |
| authorships[1].author.display_name | Himani Joshi |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Himani Joshi |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5085785393 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-8873-4644 |
| authorships[2].author.display_name | A V Subramanyam |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | A. Venkata Subramanyam |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5084951491 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-8656-4533 |
| authorships[3].author.display_name | Sumit J. Darak |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Sumit Jagdish Darak |
| authorships[3].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/1912.05255.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | SenseNet: Deep Learning based Wideband spectrum sensing and modulation classification network. |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-10-10T17:16:08.811792 |
| primary_topic.id | https://openalex.org/T12131 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9984999895095825 |
| 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 | Wireless Signal Modulation Classification |
| related_works | https://openalex.org/W2511058381, https://openalex.org/W3110130864, https://openalex.org/W3198599344, https://openalex.org/W2737970258, https://openalex.org/W2583692233, https://openalex.org/W2579022878, https://openalex.org/W2746433858, https://openalex.org/W2952162026, https://openalex.org/W2912908221, https://openalex.org/W2301265746, https://openalex.org/W2580532745, https://openalex.org/W2676890596, https://openalex.org/W2032671109, https://openalex.org/W2577570420, https://openalex.org/W2381793890, https://openalex.org/W3006493286, https://openalex.org/W1593398460, https://openalex.org/W2785833772, https://openalex.org/W2545680772, https://openalex.org/W2739537747 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2021 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | mag:2996565376 |
| 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 | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | arXiv (Cornell University) |
| best_oa_location.landing_page_url | https://arxiv.org/pdf/1912.05255.pdf |
| primary_location.id | mag:2996565376 |
| 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 | |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | arXiv (Cornell University) |
| primary_location.landing_page_url | https://arxiv.org/pdf/1912.05255.pdf |
| publication_date | 2019-12-11 |
| publication_year | 2019 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 19, 132, 146, 196 |
| abstract_inverted_index.In | 127 |
| abstract_inverted_index.We | 180 |
| abstract_inverted_index.an | 51, 237 |
| abstract_inverted_index.as | 10, 59, 190, 192 |
| abstract_inverted_index.be | 86 |
| abstract_inverted_index.in | 7, 43, 66, 100, 239 |
| abstract_inverted_index.is | 50, 108, 178, 212 |
| abstract_inverted_index.no | 173 |
| abstract_inverted_index.of | 18, 25, 54, 62, 70, 140, 176, 184, 199, 208 |
| abstract_inverted_index.on | 75, 163, 188 |
| abstract_inverted_index.to | 5, 14, 23, 80, 88, 119, 156, 215, 241 |
| abstract_inverted_index.we | 117, 130 |
| abstract_inverted_index.ADC | 97 |
| abstract_inverted_index.AMC | 73, 235 |
| abstract_inverted_index.WSS | 159, 189 |
| abstract_inverted_index.and | 39, 98, 124, 160, 170, 204 |
| abstract_inverted_index.are | 3 |
| abstract_inverted_index.can | 35, 85 |
| abstract_inverted_index.fed | 87 |
| abstract_inverted_index.for | 30, 110, 195, 223 |
| abstract_inverted_index.has | 46 |
| abstract_inverted_index.our | 185 |
| abstract_inverted_index.raw | 164 |
| abstract_inverted_index.the | 28, 67, 71, 89, 138, 154, 224 |
| abstract_inverted_index.use | 120 |
| abstract_inverted_index.Next | 0 |
| abstract_inverted_index.deep | 233 |
| abstract_inverted_index.high | 95, 101 |
| abstract_inverted_index.into | 145 |
| abstract_inverted_index.need | 29, 79 |
| abstract_inverted_index.next | 111 |
| abstract_inverted_index.part | 53 |
| abstract_inverted_index.rate | 96 |
| abstract_inverted_index.real | 44 |
| abstract_inverted_index.task | 139 |
| abstract_inverted_index.that | 34, 217, 222 |
| abstract_inverted_index.they | 84 |
| abstract_inverted_index.this | 115, 128 |
| abstract_inverted_index.time | 45, 106 |
| abstract_inverted_index.well | 191 |
| abstract_inverted_index.wide | 20, 197 |
| abstract_inverted_index.with | 91, 153, 236 |
| abstract_inverted_index.work | 74 |
| abstract_inverted_index.(AMC) | 49 |
| abstract_inverted_index.(WSS) | 58 |
| abstract_inverted_index.based | 232 |
| abstract_inverted_index.blind | 158 |
| abstract_inverted_index.input | 202 |
| abstract_inverted_index.power | 102 |
| abstract_inverted_index.prior | 174 |
| abstract_inverted_index.radio | 26 |
| abstract_inverted_index.range | 198 |
| abstract_inverted_index.sense | 36 |
| abstract_inverted_index.users | 64 |
| abstract_inverted_index.using | 209 |
| abstract_inverted_index.which | 78, 107, 136, 167 |
| abstract_inverted_index.before | 83 |
| abstract_inverted_index.called | 134 |
| abstract_inverted_index.employ | 218 |
| abstract_inverted_index.method | 151, 187 |
| abstract_inverted_index.paper, | 129 |
| abstract_inverted_index.radio) | 33 |
| abstract_inverted_index.sample | 122 |
| abstract_inverted_index.shared | 9 |
| abstract_inverted_index.signal | 93, 240 |
| abstract_inverted_index.single | 147 |
| abstract_inverted_index.Nyquist | 76 |
| abstract_inverted_index.compare | 182 |
| abstract_inverted_index.demands | 17, 94 |
| abstract_inverted_index.nyquist | 219 |
| abstract_inverted_index.operate | 6 |
| abstract_inverted_index.perform | 157 |
| abstract_inverted_index.propose | 118, 131 |
| abstract_inverted_index.reduces | 168 |
| abstract_inverted_index.results | 99 |
| abstract_inverted_index.sampled | 220 |
| abstract_inverted_index.samples | 77, 166, 211 |
| abstract_inverted_index.sensing | 57, 105, 142, 171 |
| abstract_inverted_index.support | 15 |
| abstract_inverted_index.systems | 216 |
| abstract_inverted_index.unified | 148 |
| abstract_inverted_index.variety | 21 |
| abstract_inverted_index.well as | 11 |
| abstract_inverted_index.SenseNet | 135 |
| abstract_inverted_index.accuracy | 228 |
| abstract_inverted_index.adjacent | 68 |
| abstract_inverted_index.and high | 104 |
| abstract_inverted_index.combines | 137 |
| abstract_inverted_index.compared | 214 |
| abstract_inverted_index.drawback | 116, 207 |
| abstract_inverted_index.expected | 4 |
| abstract_inverted_index.increase | 238 |
| abstract_inverted_index.learning | 234 |
| abstract_inverted_index.networks | 2 |
| abstract_inverted_index.overcome | 114 |
| abstract_inverted_index.proposed | 72, 150, 186, 225 |
| abstract_inverted_index.sampling | 231 |
| abstract_inverted_index.schemes, | 201 |
| abstract_inverted_index.shortage | 24 |
| abstract_inverted_index.sparsity | 177 |
| abstract_inverted_index.spectrum | 13, 16, 38, 41, 56, 141 |
| abstract_inverted_index.wideband | 37, 55 |
| abstract_inverted_index.based WSS | 123 |
| abstract_inverted_index.cognitive | 32 |
| abstract_inverted_index.datasets, | 203 |
| abstract_inverted_index.important | 52 |
| abstract_inverted_index.incumbent | 63 |
| abstract_inverted_index.licensed, | 8 |
| abstract_inverted_index.processed | 82 |
| abstract_inverted_index.required. | 179 |
| abstract_inverted_index.resources | 42 |
| abstract_inverted_index.spectrum, | 27 |
| abstract_inverted_index.approaches | 229 |
| abstract_inverted_index.be further | 81 |
| abstract_inverted_index.capability | 155 |
| abstract_inverted_index.complexity | 169 |
| abstract_inverted_index.estimation | 175 |
| abstract_inverted_index.generation | 1, 112 |
| abstract_inverted_index.is endowed | 152 |
| abstract_inverted_index.it enables | 60 |
| abstract_inverted_index.method,the | 226 |
| abstract_inverted_index.modulation | 125, 161, 193, 200 |
| abstract_inverted_index.time since | 172 |
| abstract_inverted_index.to Nyquist | 230 |
| abstract_inverted_index.unlicensed | 12 |
| abstract_inverted_index.consumption | 103 |
| abstract_inverted_index.extensively | 181 |
| abstract_inverted_index.significant | 206 |
| abstract_inverted_index.sub-nyquist | 121, 165, 210 |
| abstract_inverted_index.noise ratio. | 242 |
| abstract_inverted_index.pipeline.The | 149 |
| abstract_inverted_index.transmitting | 65 |
| abstract_inverted_index.unacceptable | 109 |
| abstract_inverted_index.and modulation | 143 |
| abstract_inverted_index.classification | 144, 227 |
| abstract_inverted_index.identification | 61 |
| abstract_inverted_index.locate desired | 40 |
| abstract_inverted_index.Nyquist sampled | 92 |
| abstract_inverted_index.classification. | 126 |
| abstract_inverted_index.of services.Due | 22 |
| abstract_inverted_index.the performance | 183 |
| abstract_inverted_index.classifier.Working | 90 |
| abstract_inverted_index.novel architecture | 133 |
| abstract_inverted_index.increased.Automatic | 47 |
| abstract_inverted_index.reduced performance | 213 |
| abstract_inverted_index.channel conditions.A | 205 |
| abstract_inverted_index.classification tasks | 194 |
| abstract_inverted_index.vacant spectrum.Most | 69 |
| abstract_inverted_index.modulation classifier | 48 |
| abstract_inverted_index.signal.However,we show | 221 |
| abstract_inverted_index.classification directly | 162 |
| abstract_inverted_index.communication systems.To | 113 |
| abstract_inverted_index.communication systems(like | 31 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.59942706 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |