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Record W2113242314 · doi:10.1109/lsp.2009.2016486

Near-Optimum Pilot and Data Symbols Power Allocation for MIMO Spatial Multiplexing System With Zero-Forcing Receiver

2009· article· en· W2113242314 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Signal Processing Letters · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsNovAtel (Canada)
Fundersnot available
KeywordsMIMOChannel (broadcasting)FadingPhase-shift keyingAlgorithmComputer scienceSpatial multiplexingMinimum mean square errorMultiplexingSignal-to-noise ratio (imaging)Quadrature amplitude modulationMathematicsBit error rateElectronic engineeringControl theory (sociology)TelecommunicationsStatisticsEngineeringEstimator

Abstract

fetched live from OpenAlex

Power allocation between pilot and data symbols is investigated for multiple-input and multiple-output (MIMO) spatial multiplexing (SM) system with zero-forcing receiver and minimum mean square error (MMSE) channel estimation under flat block-fading channel. Based on a modified zero-forcing receiver, which takes channel estimation error into account, a new power allocation scheme to maximize the average post-processing signal-to-noise-ratio (SNR) is proposed. This scheme only requires the computation of scalars and does not need any form of channel coefficients feedback. Simulation results show that the proposed scheme outperforms simple equal power allocation with an improvement of around 3 dB and 2 dB for QPSK and 16-QAM modulations when FER = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , respectively.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score1.000

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 head0.023
GPT teacher head0.244
Teacher spread0.222 · 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