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Record W2082018198 · doi:10.1049/iet-com.2013.1145

Spectrum‐sharing broadcast channels using fountain codes: energy, delay and throughput

2014· article· en· W2082018198 on OpenAlexaff
Yuli Yang, Sonia Aı̈ssa

Bibliographic record

VenueIET Communications · 2014
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsFountain codeThroughputFountainComputer scienceComputer networkTelecommunicationsDecoding methodsWirelessLow-density parity-check codeError floor

Abstract

fetched live from OpenAlex

In this study, the authors develop a transmission protocol for cognitive radio networks, whereby fountain codes are exploited in the broadcast channels and secondary users help with the broadcast from the base station (BS) to primary users (PUs). With fountain codes, the BS broadcasts to the secondary transmitter (ST) as well as PUs simultaneously, and stops broadcasting once the ST has received sufficient codeword to decode the original information reliably. Then, the ST will resume the broadcasting to PUs until all of them can decode the original information successfully. While broadcasting, the ST transmits information over its own link, that is, to the secondary receiver, based on dirty paper coding technique. As such, the energy expenditure at the BS is reduced and, moreover, secondary links have more opportunities to access the licensed spectrum band. To evaluate the performance of the developed scheme, they analyse its energy expenditure, broadcast time as well as the throughput over secondary links, and achieve the corresponding closed‐form expressions. Compared with the traditional broadcast protocol without the secondary's help, illustrative numerical results substantiate the validity of the author's derivations, which also demonstrate the efficiency of the developed scheme both on the energy savings and on the spectrum utilisation.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.003
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 head0.074
GPT teacher head0.315
Teacher spread0.241 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations4
Published2014
Admission routes1
Has abstractyes

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