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Record W1486516291

Évaluation de la capacité du canal UWB minier

2011· dissertation· fr· W1486516291 on OpenAlexaboutno aff

Bibliographic record

VenueDepositum (Université du Québec en Abitibi-Témiscamingue) · 2011
Typedissertation
Languagefr
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

La capacité du canal permet de caractériser les performances maximales d’un canal de transmission, le nombre maximal de bits susceptible d’être transmis par seconde. Pour un canal donné, l’UWB peut garantir un grand débit pour la transmission de données. Le canal de transmission UWB est généralement un canal à trajets multiples, surtout pour les applications à l’intérieur. Aussi, la réponse de ce canal est sélective dans le
\ndomaine fréquentiel. Pour étudier la capacité de n’importe quel canal il faut d’abord le caractériser et le modéliser. Dans notre recherche, on va se baser sur les mesures faites
\npar Chehri et Fortier dans la mine CANMET à Val-d’Or qui est située à 500 km au nord de Montréal, Canada. Ces mesures montrent que la distribution Nakagami donne
\nun bon ajustement pour l’amplitude du signal reçue à petit échelle.
\nLa formule classique de la capacité de Shannon est obtenue pour les canaux ayant des réponses fréquentielles plates. Cette formule ne s’applique pas directement dans
\nnotre modèle de canal. Pour utiliser la formule classique de la capacité de Shannon, nous devons d’abord diviser la bande de fréquences en un nombre très grand (théoriquement
\ninfini) de petites bandes, considérées comme des sous-canaux à réponse plates dans le domaine fréquentiel. Ensuite, on peut appliquer une distribution optimale de
\npuissance maximisant la capacité pour une puissance d’émission totale limitée. Cette méthode est connue sous le nom de "waterfilling".
\nLes travaux antérieurs sur l’évaluation de la capacité du canal UWB en externe (outdoor) n’ont pas tenu compte des évanouissements du canal, et en interne (indoor), le
\ncas d’un milieu UWB minier n’a pas encore été abordé. Dans ce mémoire de maîtrise on s’est particulièrement intéressé au problème d’évaluation de la capacité du canal UWB
\nminier. En utilisant la méthode "waterfilling" on a calculé la capacité d’un canal UWB minier d’une manière optimale en tenant compte des caractéristiques d’évanouissement
\ndu canal. Les résultats obtenus prouvent la pertinence de la méthode "waterfilling" dans ces type des canaux ; cette méthode donne une amélioration importante de la capacité,
\nd’un facteur entre 1.1 à 1.22 fois plus grand que la capacité uniforme lorsque le SNR < 40 dB. Lorsque le SNR > 40 dB, la capacité optimale et la capacité uniforme convergent, et on remarque que les deux méthodes donnent les mêmes résultats lorsque le rapport signal sur bruit est grand (> 80 dB).
\n
\nCapacity plays an important role in characterizing the maximum performance for channel transmission by providing the maximum number of bits that can be transmitted per second. Furthermore, for a given channel, a large rate for data transmission can be guaranteed using UWB modulation.
\nIn fact, the UWB transmission channel is generally a multipath channel especially for indoor applications. Thus, the channel response is selective in the frequency domain.
\nTo be able to study the capacity of any channel, it should be characterized and modeled. In this research, we depend on the measures taken by Chehri and Fortier in the CANMET mine in Val-d’Or, located 500 km north of Montreal, Canada. These measurements show that the Nakagami distribution gives a good adjustment for the amplitude of the signal received at a small scale.
\nIndeed, the classical formula for the Shannon capacity is used for flat channels. Thus, we can first divide the whole frequency band into many small bands, in which
\nthe sub-channel can be considered frequency-flat. After that, we can apply an optimal distribution of power to maximize the capacity of total transmission over limited power; this method is known as "waterfilling".
\nPrevious works on the evaluation of UWB channel capacity considered the external (outdoor) case, however, they did not consider the fading channel. Also, the internal
\n(indoor) studies did not discuss the case of the mining channel. In this thesis, we paid particular attention to the problem of evaluating the UWB channel in the mine. By
\nusing the "waterfilling" method, we calculated the capacity of a UWB channel mining optimally, taking into account the characteristics of the fading channel. The results
\ndemonstrate the relevance of the "waterfilling" method in these types of channels. We show that, when the transmitted signal-to-noise ratio (SNR) is lower than 40 dB, using
\noptimal power spectrum allocation at the transmitter side can increase transmission rate compared to the uniform power spectrum allocation scheme. Whereas, when the
\ntransmitted SNR is higher than 80 dB, the benefit of optimal power spectrum allocation is very limited.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0020.002
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 head0.005
GPT teacher head0.174
Teacher spread0.169 · 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 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
Published2011
Admission routes1
Has abstractyes

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