MétaCan
Menu
Back to cohort
Record W2039911522 · doi:10.1109/lsp.2014.2378773

Slow Adaptive Power Control and Outage Avoidance in Composite Fading Wireless Channels

2014· article· en· W2039911522 on OpenAlexaff
Younes Seyedi, Ehsan Bolouki, Jean‐François Frigon

Bibliographic record

VenueIEEE Signal Processing Letters · 2014
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFadingComputer scienceFading distributionPower controlTransmitter power outputPhase-shift keyingWirelessElectronic engineeringChannel state informationPower (physics)Control theory (sociology)TelecommunicationsBit error rateChannel (broadcasting)EngineeringTransmitterRayleigh fadingPhysicsControl (management)

Abstract

fetched live from OpenAlex

Composite fading wireless channels possess both fast and slow fading dynamics. In such channels, the average bit error probability varies slowly over time, hence, it is feasible to harness a power control mechanism to effectively avoid the bit error outage (BEO). In this letter, we focus on a new adaptive power allocation scheme for M-ary phase shift keying ( M-PSK) signals. The proposed power control technique is designed to track the moments of the composite fading envelope. Analytical expressions are derived which provide the optimal transmit power (under long-term power constraint) and the minimum transmit power to cope with the BEO. Numerical results indicate that the transmit power can be potentially decreased depending on the fading gain observed in the coherent and diffuse components of the received signal.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Signal Processing LettersSame topicWireless Communication Networks ResearchFrench-language works237,207