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Record W1524360447 · doi:10.1109/wimob.2005.1512810

Output SIR distribution of optimum combining in Rayleigh fading channels with channel estimation errors

2006· article· en· W1524360447 on OpenAlexaff
Amir Ali Basri, Teng Joon Lim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRayleigh fadingProbability density functionFadingChannel (broadcasting)Monte Carlo methodCumulative distribution functionStatisticsInterference (communication)AlgorithmComputer scienceMathematicsTelecommunications

Abstract

fetched live from OpenAlex

This paper investigates the effect of imperfect channel estimates on the distribution of signal to interference ratio (SIR) at the output of the optimum combiner for space diversity reception with multiple interferers in a flat Rayleigh fading environment. Channel estimation errors result in flawed optimum combiner weights degrading the system performance. We consider interference-limited systems in which the number of interferers is no less than the number of antenna elements. It is assumed that the channel estimation errors are circularly symmetric Gaussian distributed, and the interferers have equal powers. The main contribution of the paper is applying multivariate statistical analysis to derive an exact closed-form expression for the probability density function of the output SIR. The analytical result is a useful tool to find the impact of imperfect channel estimation on several measures of performance such as average SIR, outage probability and average bit error probability. The analytical expression is verified by Monte Carlo simulations.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score0.519

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)0.0000.000

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 head0.012
GPT teacher head0.233
Teacher spread0.221 · 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
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

Citations9
Published2006
Admission routes1
Has abstractyes

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