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Record W2148067298 · doi:10.1109/vetecs.2008.204

Radiation Footprint Minimization Using Encoded OFDM Pilots for Cognitive Radio Communications

2008· article· en· W2148067298 on OpenAlexaff
Xianbin Wang, P. Ho, Jie Zhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsSimon Fraser UniversityWestern University
Fundersnot available
KeywordsCognitive radioOrthogonal frequency-division multiplexingComputer scienceTransmitterTransceiverElectronic engineeringWirelessFootprintInterference (communication)Transmission (telecommunications)Pilot signalTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

The limited availability of spectrum and the inefficiency of its usage necessitate new research on spectral opportunistic communication technologies including cognitive radio (CR). Such new systems are characterized by the coexistence of the heterogeneous wireless systems. In this paper, a radiation footprint minimization technique is proposed through encoded in-band pilot tones for Orthogonal frequency division multiplexing (OFDM) system. A transmission power negotiation signaling between the transmitter and receiver is established through the encoded pilot tones. Electromagnetic interference to the primary and other cognitive radios can be minimized with an automatically reduced radiation footprint. In addition, system performance of the receiver can be guaranteed in the process of mutual interference minimization. The transceiver structure and the inband pilot tone detection algorithm are investigated. The principle and performance of the system are validated through numerical 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.

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 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 score0.981
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.068
GPT teacher head0.294
Teacher spread0.226 · 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 designSimulation or modeling
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
Published2008
Admission routes1
Has abstractyes

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