Relay selection scheme with adaptive cyclic prefix for cooperative amplify-and-forward relay
Bibliographic record
Abstract
In cooperative amplify-and-forward (AF) relay networks, the system performance over multipath channels is impacted by both frequency-selective fading and delay spread. However, most relay selection (RS) schemes choose the best relay node only based on the channel gain while ignoring the delay spread effect on the performance. If orthogonal frequency division multiplexing (OFDM) is used in AF relay networks, the cyclic prefix (CP) length has to be extended to tolerate the accumulated delay spread from source via relay to destination due to the lack of the channel compensation at relay nodes. A long CP, which is the transmission overhead, redeces both the effective data transmission throughput and the overall system transmission efficiency. Therefore, an appropriate RS scheme in cooperation multiple-relay networks should not only enhance the overall transmission reliability but also minimize the relay overhead. To this end, we propose a variable-CP based RS scheme for AF relay networks to maximize the transmission efficiency by dynamically choosing the most suitable relay node. In the proposed scheme, a normalized effective throughput is defined as the selection criterion which depends on both the end-to-end channel gain and the accumulated delay spread. Based on this criterion, the best relay link is selected by achieving the tradeoff between the transmission reliability and overhead. Both the theoretical analysis and simulation results show that when the channel delay spread varies, the proposed scheme can dramatically improve the the effective data transmission throughput compared to the maximum signal-to-noise-ratio schemes with variable/fixed CP.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".