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Record W4389065315 · doi:10.1109/ojvt.2023.3336619

Capacity Analysis of UAV-to-Ground Channels With Shadowing: Power Adaptation Schemes and Effective Capacity

2023· article· en· W4389065315 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 Open Journal of Vehicular Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFadingTransmitterChannel (broadcasting)Ergodic theoryChannel capacityComputer sciencePower (physics)ErgodicityInversion (geology)Topology (electrical circuits)Adaptation (eye)Control theory (sociology)Electronic engineeringTelecommunicationsMathematicsEngineeringElectrical engineeringPhysicsStatisticsArtificial intelligenceMathematical analysis

Abstract

fetched live from OpenAlex

In this paper, an unmanned aerial vehicle (UAV), acting as a transmitter, employs different power adaptation strategies in order to enhance the ergodic capacity of the wireless channel between it and a receiver on the ground. We present the derivation of closed-form expressions for the channel capacity of the recently developed UAV-to-ground fading channels under different power adaptation strategies. The power adaptation strategies considered in this paper are optimal rate adaptation with fixed power (ORA), optimal power and rate adaptation (OPRA), channel inversion with fixed rate (CIFR), and truncated channel inversion with fixed rate (TIFR). In addition to ergodic capacity analysis, precise analytical formulas for the effective capacity of the UAV-to-ground fading channels are derived. Additionally, all of these closed-form expressions are verified by comparing them with numerical results obtained through Monte Carlo simulations.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.240
Teacher spread0.222 · 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