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Record W4405099163 · doi:10.22215/etd/2024-16272

Enhanced Uplink Communications in 5G Cellular Connected UAV Networks Using Machine Learning

2024· dissertation· en· W4405099163 on OpenAlexaff
Fatemeh Banaeizadeh

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsBase stationTelecommunications linkMIMOComputer scienceCellular networkComputer networkUser equipmentWireless networkReinforcement learningWirelessLow latency (capital markets)Real-time computingChannel (broadcasting)TelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

There have been substantial advancements in Fifth Generation (5G) and Beyond-5G (B5G) wireless communications, marked by key technologies such as Millimeter-Wave (mmWave), Non-orthogonal Multiple Access (NOMA), and Massive Multiple-Input Multiple-Output (MIMO).These innovations address user demands for ubiquitous connectivity, high throughput, low latency, and a reliable and secure connection.However, deploying these technologies successfully presents challenges, including modeling propagation channels, ensuring eective user coverage, detecting pilot contamination attacks in MIMO systems, and managing inter-user interference.This thesis aims to tackle the latter two issues.

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 categoriesMeta-epidemiology (narrow)
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 score0.835
Threshold uncertainty score1.000

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 science0.0000.000
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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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