Performance of selective space‐time coding and selection diversity under perfect and imperfect CSI
Bibliographic record
Abstract
Abstract Selective space‐time coding and selection diversity can be viewed as practical means to reduce the implementation complexity of multiple‐input multiple‐output (MIMO) systems while still taking benefit of the use of multiple antennas. In this paper, we evaluate the performance of selective space‐time block coding (selective‐STBC) and antenna selection diversity, and analyze the performance of both techniques under perfect and imperfect channel state information (CSI) available at both ends of the transmission link. Our performance analysis reveals that, under perfect or imperfect CSI and ideal feedback channel, selective‐STBC yields a loss in selection diversity gains and that selecting just a single antenna at the transmitter side is the best transmission strategy. We also show that selective‐STBC and antenna selection diversity have different behaviors when the feedback channel is imperfect. Indeed, it is shown that selection diversity outperforms selective‐STBC when the feedback channel is of high quality, while selective‐STBC yields better performance when the feedback channel is of low quality. Copyright © 2008 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".