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Brain-Inspired Dynamic Spectrum Management for Cognitive Radio Ad Hoc Networks

2012· article· en· W1964171670 on OpenAlex
Farhad Khozeimeh, S. Haykin

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 Transactions on Wireless Communications · 2012
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCognitive radioComputer scienceWireless ad hoc networkComputer networkMemorizationExploitScheme (mathematics)Mobile ad hoc networkSimple (philosophy)Channel (broadcasting)Distributed computingWirelessTelecommunicationsComputer security

Abstract

fetched live from OpenAlex

This paper introduces the self-organizing dynamic spectrum management (SO-DSM) for ad hoc cognitive radio (CR) networks. In this scheme, CR units try to exploit the primary networks' unused bands by establishing links with neighbouring CRs using those bands. Inspired by human brain, the CRs extract and memorize the activity patterns of the primary network and other CRs and create temporal channel assignments on sub-bands with no recent primary user activities using a self-organizing maps (SOM) technique. The proposed scheme is decentralized and employs a simple learning rule with low complexity and minimal memory requirements. The simulation results show that SO-DSM significantly decreases the probability of collision with primary users and also probability of CR link interruption.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.979
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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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