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Record W2071032643 · doi:10.1109/msna.2012.6324523

Design and simulation of a variable bit load adaptive OFDM transceiver for frequency selective channel

2012· article· en· W2071032643 on OpenAlexaff
H. Charafeddine, Voicu Groza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingComputer scienceChannel (broadcasting)TransceiverAdaptive beamformerElectronic engineeringBeamformingLeast mean squares filterPhase-shift keyingBit error rateSignal-to-noise ratio (imaging)Interference (communication)Modulation (music)Mean squared errorAlgorithmAdaptive filterEngineeringTelecommunicationsMathematicsWirelessAcousticsStatistics

Abstract

fetched live from OpenAlex

This paper presents the design and simulation of an adaptive LMS beamformer for OFDM receiver array in frequency selective channel. The adaptive array uses the least mean square (LMS) algorithm for beamforming, which minimizes the mean square error (MSE) between the received pilot signal and the reference one. The LMS system proofed to be very efficient in interference cancellation, steering toward the signal direction of arrival (DOA), and improving the signal to noise ratio (SNR). In addition an adaptive bit loading algorithm is also proposed in this paper which improves the overall throughput of the algorithm by assigning higher order modulation schemes to subcarriers with high SNR and more robust modulation schemes such as BPSK or DBPSK to subcarriers with low SNR. The performance of the suggested OFDM system is investigated and compared to the conventional OFDM system.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.616
Threshold uncertainty score0.315

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 communication0.0000.001
Open science0.0000.000
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 head0.037
GPT teacher head0.277
Teacher spread0.239 · 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 designSimulation or modeling
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
Published2012
Admission routes1
Has abstractyes

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