Multiple‐frame precoding and multi‐D mapping for BICM over ergodic NAF relay channels
Bibliographic record
Abstract
ABSTRACT This paper proposes the idea of precoding over multiple cooperative frames with multi‐dimensional (multi‐D) mapping for a bit interleaved coded modulation system over an ergodic non‐orthogonal amplify‐and‐forward (NAF) half‐duplex single‐relay channel. The benefits of multiple‐frame precoding and multi‐D labeling are analyzed in two different regions: the error‐floor region to exploit diversity and the turbo pinch‐off region for near‐capacity performance. In the error‐floor area, it is shown that the diversity gain function of the considered system is th power of that of uncoded cooperative systems, where Nf is the number of precoded cooperative frames and dH is the minimum Hamming distance of the outer code. To optimize the asymptotic coding gain, it is then shown that the source and relay must transmit orthogonally for best asymptotic performance. In the turbo pinch‐off region, we demonstrate that the proposed system concatenated with a simple outer binary code can be employed to achieve near‐capacity performance. Using union bounding techniques, we show that the optimal multi‐D labeling for NAF relaying shall maximize the average Euclidean distance between all pairs in the multi‐D rotated constellation whose labels differ in only 1 bit. The extrinsic information transfer charts are then used to match the outer code, the multi‐D mapping, and the precoder. It is demonstrated that the proposed system is also promising for NAF relaying in the turbo pinch‐off region, in the sense that it can operate below the achievable rate achieved by a conventional coded modulation scheme. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".