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Record W1671096031 · doi:10.1109/iwcmc.2015.7289104

Impact of Hidden Collision on primary users' performance in Dynamic Spectrum Access

2015· article· en· W1671096031 on OpenAlexaff
Marouane Sebgui, Jalal Almhana, Slimane Bah, Belhaj El Graini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsThroughputCarrier sense multiple access with collision avoidanceComputer scienceComputer networkCollisionMultiple Access with Collision Avoidance for WirelessAccess controlHidden node problemIdleTransmission (telecommunications)Collision avoidanceCollision problemCapture effectProtocol (science)Focus (optics)Computer securityWirelessTelecommunicationsOperating systemWireless network

Abstract

fetched live from OpenAlex

Dynamic Spectrum Access (DSA) allows Secondary Users (SUs) to access a shared medium when it is in idle state, however, several challenges may be encountered. In this paper, we will focus on the Hidden Collision problem (HC) which occurs when Primary Users (PUs) are using Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) procedure, such as 802.11 protocol, and when SUs are trying to access the medium during the Backoff Window (BW) as they wrongly perceived the medium as available. As a consequence, PUs' performance may be negatively affected. In previous work, we proposed a new model that adresses this HC problem and allows better control of its effect. In this paper, we extend our work and study the effect of HC on PUs' performance in terms of throughput and delays under controlled and uncontrolled HC. Our results show that both throughput and delays decrease with the increase of SU transmission activity. The number of PUs has little effect on their throughput and a more significant impact on their traffic delays.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.317

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 communication0.0000.001
Open science0.0010.000
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 head0.030
GPT teacher head0.321
Teacher spread0.291 · 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 designObservational
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
Published2015
Admission routes1
Has abstractyes

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