Regularized WDFDC Receivers for Selective Detect-and-Forward Multi-Relaying Systems
Bibliographic record
Abstract
This article considers regularized Weighted Decision Feedback Differential Coherent (WDFDC) receivers for selective Detect-and-Forward multi-relaying systems, operating over fast fading channels. Non-regularized WDFDC receivers have been employed in such One-Way Relay Network (OWRN) with DQPSK modulation, and shown to provide significant performance gains over Conventional Differential Detection (CDD). This paper demonstrates, however, that such non-regularized WDFDC receivers are plagued by a BER increase phenomenon in the high SNR range due to decision feedback error propagation and intermittent transmissions from relays. Because of this effect, the non-regularized WDFDC receivers are unable to provide very low error rates, making them unsuitable for ultra-reliable communication systems. To address this problem, our paper introduces a novel WDFDC receiver based on a regularized linear predictor (RLP) for relay to destination channels. We show that such regularized WDFDC receivers yield significant performance gains over their non-regularized counterparts in the high SNR range, without noticeable degradation at low SNR. Regularized WDFDC receivers on relay to destination links enable OWRN systems to provide very low error rates, making them suitable for ultra-reliable communication over fast fading channels.
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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 | 0.000 |
| 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 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".