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Record W2038604924 · doi:10.1109/icc.2010.5502764

Design of Multiuser Pre-Rake Systems for Reliable Ultra-Wideband Communications

2010· article· en· W2038604924 on OpenAlexaff
Zahra Ahmadian, Michael Botros Shenouda, Lutz Lampe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRakeComputer scienceRake receiverBase stationTransmission (telecommunications)Electronic engineeringInterference (communication)Multiuser detectionWidebandMinimum mean square errorCommunications systemComputer networkTelecommunicationsChannel (broadcasting)Code division multiple accessEngineeringFadingMathematics

Abstract

fetched live from OpenAlex

We consider the design of ultra-wideband (UWB) systems that provide high capacity communication for short-range wireless applications. The design configuration is a multiuser pre-rake UWB broadcast communication system in which the base station is equipped with multiple antennas to achieve high data rates while each user is equipped with a simple and cost-efficient single antenna receiver. We assume the use of multiuser pre-equalization filters at the base station to mitigate the effect of inter-symbol interference (ISI) and multiuser interference (MUI) at the receivers. For the optimization of these filters we develop an analytical framework that minimizes the total transmission power while satisfying (physical layer) users' quality of service for reliable communications. In particular, the quality of service constraints are expressed in terms of the mean square error (MSE) for each user. We show that the design problem is equivalent to a convex optimization problem that can be solved efficiently. The numerical studies confirm that the proposed design strategy for multiuser pre-equalization filters enables the reduction of the total transmission power to achieve a given set of requested MSE targets.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.537

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 science0.0010.000
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 head0.022
GPT teacher head0.249
Teacher spread0.227 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations1
Published2010
Admission routes1
Has abstractyes

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