Performance analysis of multi‐input multi‐output systems with maximum likelihood detection over shadowed fading channels
Bibliographic record
Abstract
Summary Multiple‐input multiple‐output (MIMO) transmission techniques constitute an important technology in modern wireless communication. Hence, performance analysis methods for such systems are of considerable interest. This paper considers first the average pairwise error probability for uncoded MIMO systems employing maximum likelihood detection over a composite Rayleigh‐Lognormal fading channel with spatial correlation. It provides general results, applicable also to a wider class of shadowing models, concerning asymptotical diversity gains and shows that they are not changed by such shadowing. Then, analytical evaluation techniques for bit‐error‐rate (BER) over composite Rayleigh‐Lognormal fading channels, based on the truncated union bound and the transfer function, are considered. Furthermore, these techniques are modified for applications over spatially correlated channels. This paper shows that such performance evaluation techniques provide good approximations to BER of spatially uncorrelated MIMO systems and also in the presence of moderate spatial correlation, over Rayleigh‐Lognormal fading channels. Copyright © 2013 John Wiley & Sons, Ltd.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Performance analysis of MIMO wireless systems; the 'methods' are engineering analysis techniques, not research methods.
The study analyzes error performance in wireless communication systems.
Performance analysis of MIMO wireless systems; communications engineering.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".