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Record W2287608525 · doi:10.1109/tcomm.2015.2506698

Energy Efficiency of Adaptive HARQ

2015· article· en· W2287608525 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Communications · 2015
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMcGill UniversityInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsHybrid automatic repeat requestComputer scienceTransmitterRayleigh fadingDecoding methodsTransmission (telecommunications)Channel state informationChannel (broadcasting)FadingAutomatic repeat requestTransmitter power outputLink adaptationAdaptive codingReal-time computingComputer networkElectronic engineeringAlgorithmWirelessTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this work, various channel coding schemes that can be used in hybrid automatic repeat request (HARQ) transmission protocols are investigated from an energy efficiency point of view. Conventional HARQ, where only one bit is used to inform the transmitter about the decoding success or failure, is compared to adaptive HARQ where the transmitter adapts either the length or the transmit power of the codewords using outdated channel state information (i.e., experienced by the receiver during the past transmissions). Describing the problems within a Markov decision process framework, we find optimal adaptation policies for both persistent (unlimited number of transmission) and truncated HARQ protocols. Numerical examples obtained in a Rayleigh block fading channel show that, in terms of energy efficiency, the adaptation of the codewords length provides notable gains over power adaptation and conventional HARQ.

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.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.000
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 head0.108
GPT teacher head0.303
Teacher spread0.196 · 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