Diversity and Delay Analysis of Buffer-Aided BICM-OFDM Relaying
Bibliographic record
Abstract
In this paper, we study a cooperative diversity scheme for wireless systems where the relay is equipped with a buffer. We consider practical frequency—selective channels and adopt the combination of bit interleaved coded modulation and orthogonal frequency division multiplexing (BICM—OFDM). We propose a novel link selection protocol for BICM—OFDM systems where the relay either transmits or receives in a given time slot depending on the quality of the links. We derive a closed—form upper bound for the asymptotic worst—case pairwise error probability (PEP) and the diversity gain of the considered buffer—aided relaying scheme for both infinite and finite buffer size. We show that significant diversity gains can be achieved with buffer—aided relaying compared to conventional relaying at the expense of larger packet delays. In fact, for buffers of infinite (or very large) size, the diversity gain is doubled for links with identical frequency diversity, and an even higher diversity gain advantage is possible for links with non—identical frequency diversities. Furthermore, we perform an exact closed—form average delay analysis for buffers of both finite and infinite size which provides important insight into the achieved delay—performance tradeoff. The derived analytical results and performance gains are corroborated by extensive simulation results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".