MétaCan
Menu
Back to cohort
Record W3019936847 · doi:10.1109/mwc.001.1900174

mmWave IEEE 802.11ay for 5G Fixed Wireless Access

2020· article· en· W3019936847 on OpenAlexaff
Khalid Aldubaikhy, Wen Wu, Ning Zhang, Nan Cheng, Xuemin Shen

Bibliographic record

VenueIEEE Wireless Communications · 2020
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer networkBroadbandWireless networkInternet accessWirelessInter-Access Point ProtocolIEEE 802Wireless broadbandTelecommunicationsThe InternetWi-FiQuality of service

Abstract

fetched live from OpenAlex

Fixed wireless access (FWA) utilizing both licensed and unlicensed millimeter wave (mmWave) spectrum is considered a key technology that can lead to the early deployment of the fifth-generation new-radio (5G-NR) networks. 5G FWA can provide easy installation of network infrastructure and ubiquitous high-speed Internet access at low cost compared to the conventional broadband fixed access networks. In this article, we investigate the mmWave distribution network (mDN) use case that has been standardized recently by the IEEE 802.11ay standard as an alternative 5G FWA solution. Specifically, we provide a comprehensive tutorial view of the considered new protocol specifications and design elements of the mDN. We also highlight some challenging research issues in the field of the mDN. Finally, we provide a case study based on the investigation of the mDN where a low-complexity concurrent transmission protocol is proposed to enhance the network performance while mitigating the interference.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.125
GPT teacher head0.310
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations66
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Wireless CommunicationsSame topicMillimeter-Wave Propagation and ModelingFrench-language works237,207