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Record W2917607308 · doi:10.1109/isspit.2018.8642633

Narrowband Data Transmission in TV White Space: An Experimental Performance Analysis

2018· article· en· W2917607308 on OpenAlexaffabout
Dewan Md. Ariful Hassan, Danilo Corral-De-Witt, Sabbir Ahmed, Kemal Tepe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNarrowbandWhite spacesTransmission (telecommunications)Computer scienceSpace (punctuation)White (mutation)TelecommunicationsWireless

Abstract

fetched live from OpenAlex

TV White Spaces (TVWS) are UHF or VHF TV channels that are not under use by TV broadcasters at a particular time. As a means of achieving the global objective of efficient utilization of expensive spectrum bandwidth, TVWS can be exploited by Cognitive Radio (CR) enabled Software Defined Radio (SDR) systems for alternative services like communications for disaster-time rescue teams, Internet service for hard-to-reach communities etc. In this contribution, we report the design and prototype development of a testbed for real-time testing of secondary user transmission in TVWS. Once an unused TV channel has been identified, our system uses t hat idle channel for transmitting and receiving a narrowband signal. The testbed is built on Universal Software Radio Peripheral (USRP) 2901 device powered by GNU Radio software, RTL SDR 2832U and Spectrum Analyzer Tektronix MDO4054-3. Using 25kHz narrow bands within the TV channel#23 in Windsor, ON, Canada, and considering text, voice, and image data, we show channel and transmission characteristics through measured Bit Error Rate (BER) and constellation diagrams. Based on the experimental results, we affirm that it is possible to use this approach to successfully implement narrowband dynamic spectrum access (DSA) by secondary users over an idle TV channel.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.895

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.293
Teacher spread0.261 · 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
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

Citations3
Published2018
Admission routes2
Has abstractyes

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