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Record W1967104245 · doi:10.1109/infcomw.2010.5466719

Receiver-Aided Spectrum Sensing Scheme with Spatial Differentiation in OFDM Based Cognitive Radio Networks

2010· article· en· W1967104245 on OpenAlexafffund
Weiwei Wang, Jun Cai, Attahiru Sule Alfa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCognitive radioOrthogonal frequency-division multiplexingComputer scienceInterference (communication)TransmitterOverhead (engineering)Channel (broadcasting)Electronic engineeringPartially observable Markov decision processSignal-to-noise ratio (imaging)TelecommunicationsWirelessMarkov chainMarkov modelEngineering

Abstract

fetched live from OpenAlex

In this paper, a novel spectrum sensing scheme, called receiver-aided spectrum sensing scheme with spatial differentiation (RaSSSD), is proposed for orthogonal frequency division multiplexing (OFDM) based cognitive radio networks. In RaSSSD, the deployed area of secondary users (SUs) is divided into two sub-areas. By determining which sub-area secondary user transmitter (SUT) and secondary user receiver (SUR) are located through measuring the received signal-to-noise radio (SNR) of primary signal, RaSSSD applies different strategies for channel sensing with the aid of SUR if feasible. Such aid from SUR is implemented through feeding back a state indicator, whose short length would not introduce high overhead and interference. In addition, the theory of partially observable Markov process (POMDP) is used to determine the optimal sub-channel for sensing and access. Numerical results demonstrate that with the proposed RaSSSD, the throughput of SUs can be significantly improved under the limitation of interference to the primary users.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
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 head0.007
GPT teacher head0.209
Teacher spread0.201 · 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

Citations5
Published2010
Admission routes2
Has abstractyes

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