Transparent Amplify-and-Forward Relaying in MIMO Relay Channels
Bibliographic record
Abstract
In this paper, we investigate the performance of a single-relay assisted cooperative link, where the source, relay, and destination terminals are equipped with multiple transmit and receive antennas. We focus on the so-called transparent amplify-and-forward (T-AaF) for MIMO relay channels, in which the relay requires neither channel state information (CSI) nor synchronization of symbols or carrier. Specifically, we propose and analyze new selection schemes, where the relay, based on the received signal energies, selects the "best" received signal on the S → R hop and forwards it to the destination terminal with either repetitive or selective transmission. Through the derivation of pairwise error probability (PEP) expressions and asymptotic order of diversity (AOD) analysis, we demonstrate that the proposed selection schemes preserve full diversity order, while maintaining low complexity implementation, which makes them attractive from a practical point of view. Furthermore, we show that adopting selective transmission on the R → D hop provides better performance, better throughput, and significantly better energy efficiency than repetitive transmission. A Monte Carlo simulation study is also presented to corroborate the analytical results and to provide detailed performance comparisons between the two schemes under consideration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".