MétaCan
Menu
Back to cohort
Record W1967061054 · doi:10.1109/pimrc.2011.6140094

OFDM-UWB MIMO transceiver implementation in realistic fading channels

2011· article· en· W1967061054 on OpenAlexaff
Mohamed AlJerjawi, Yansheng Xu, R.G. Bosisio, Chahé Nerguizian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMIMOTransceiverComputer scienceElectronic engineeringMIMO-OFDMFadingOrthogonal frequency-division multiplexingSpatial multiplexingWidebandChannel (broadcasting)Wireless3G MIMOTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

This paper proposes the implementation of an Orthogonal Frequency-Division Multiplexing (OFDM) Ultra-wideband (UWB) multiple-input multiple-output (MIMO) transceiver employing a novel design of Wave-Radio Interferometer (WRI) circuit as a direct down-converter. In order to investigate the performance of the system in a realistic environment, the transceiver is simulated and tested in laboratory using an UWB fading channel defined by the IEEE802.15.3a standard set for high-rate wireless-personal area networks (WPANs). According to these standard specifications, a MATLAB code has been used to generate the channel model for simulations. The same code representing the channel impulse response has been imported to a radio channel emulator to imitate the wireless channel behavior for the laboratory measurements. For the proposed transceiver operating in the frequency range (3.1-4.1GHz), single-input multiple-output (SIMO) and MIMO configurations have been considered. A comparative study between standard channel models (CM) 1 and 4 is presented for each scenario. The results demonstrate a significant performance enhancement when an extra branch is used at the receiver of the SIMO, MIMO systems. This is due to the advantage of added diversity obtained in multiple-output systems.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.565

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 science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.255
Teacher spread0.225 · 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
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

Explore more

Same topicUltra-Wideband Communications TechnologyFrench-language works237,207