MétaCan
Menu
Back to cohort
Record W2296439027 · doi:10.1109/tvt.2016.2517042

Modeling and Analysis of Cooperative Relaying in Spectrum-Sharing Cellular Systems

2016· article· en· W2296439027 on OpenAlexafffund
Minghua Xia, Sonia Aı̈ssa

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2016
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceElectronic engineeringEngineeringComputer network

Abstract

fetched live from OpenAlex

In this paper, spectrum-sharing technology is integrated into cellular systems to improve spectrum efficiency. Macrocell users are primary users (PUs), whereas those within local cells, e.g., femtocell users, or desiring cost-effective services, e.g., roamers, are identified as secondary users (SUs). The SUs share the spectrum resources of the PUs in an underlay way; thus, the transmit power of a secondary is strictly limited by the primary's tolerable interference power. Given such constraints, a cooperative relaying transmission between an SU and the macrocell base station (BS) is necessary. To guarantee the success of dual-hop relaying and avoid multihop relaying, a new cooperative paradigm is proposed, where an idle PU (instead of a secondary, as assumed in general) in the vicinity of a target SU is chosen to serve as a relaying node, due to the fact that any PU can always transmit to the macrocell BS directly. Moreover, a two-way relaying strategy is applied at the chosen relaying node to further improve spectral efficiency. Our results demonstrate that the proposed system is particularly suitable for delay-tolerant wireless services with asymmetric downlink/uplink traffic, such as e-mail checking, web browsing, social networking, and data streaming, which are the most popular applications for SUs in spectrum-sharing cellular networks.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.688
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.249
Teacher spread0.225 · 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 teacher head, 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

Citations19
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Vehicular TechnologySame topicCooperative Communication and Network CodingFrench-language works237,207