Delay constrained buffer-aided relaying with outdated CSI
Bibliographic record
Abstract
Recent studies have shown that buffer-aided relaying with adaptive link selection can provide significant performance gains compared to conventional relaying using a fixed transmission schedule. In this paper, we focus on error rate analysis of adaptive link selection for a three node decode-and-forward (DF) relay network with fixed-rate transmission. As in practice link selection may be performed based on outdated channel state information (CSI) because of a delay in the feedback link, we study the error rate performance for both perfect and outdated CSI. Since a packet transmission delay is unavoidable with opportunistic link selection, we provide a unified error-rate analysis in terms of a decision threshold β which can be adjusted to achieve buffer stability and a desired average system delay. We show that a diversity gain of two can be achieved for perfect CSI and the optimum BER can be approached even if only a small delay is tolerated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".