Performance Analysis Framework for Transmit Antenna Selection Strategies of Cooperative MIMO AF Relay Networks
Bibliographic record
Abstract
The performance of three transmit antenna selection (TAS) strategies for dual-hop multiple-input-multiple-output (MIMO) ideal channel-assisted amplify-and-forward (AF) relay networks is analyzed. All channel fades are assumed to be Nakagami-m(integerm) fading. The source, relay, and destination are MIMO terminals. The optimal TAS and two suboptimal TAS strategies are considered. Since direct analysis of the end-to-end signal-to-noise ratio (e2e SNR) of the optimal TAS is intractable, a lower bound of the e2e SNR is derived. Its cumulative distribution function and the moment generating function (mgf) are derived and used to obtain the upper bounds of the outage probability and the average symbol error rate (SER). For the two suboptimal TAS strategies, we derive the exact mgfs of the e2e SNR and obtain accurate and efficient closed-form approximations for the outage probability and the average SER. The asymptotic outage probability and the average SER, which are exact in high SNR, are also derived, and they provide valuable insights into the system design parameters, such as diversity order and array gain. The exact outage probability, average SER, and their high SNR approximations are also derived for the optimal TAS when the direct path is ignored. The impact of outdated channel state information (CSI) on the performance of TAS is also studied. Specifically, the amount of performance degradation due to feedback delays is studied by deriving the asymptotic outage probability and the average SER and thereby quantifying the reduction of diversity order and array gain. Numerical and Monte Carlo simulation results are provided to analyze the system performance and verify the accuracy of our analysis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".