Error recovery with soft value combining for wireless cooperative systems
Bibliographic record
Abstract
Due to the multi-path, multi-hop transmission structure in wireless cooperative systems, when packet errors occur, retransmissions may take longer time and more energy, and thus affect the system performance and user-perceived quality of services. How to reduce the error rate for cooperative systems is a critical issue. In this paper, a novel and simple error recovery scheme for wireless cooperative systems has been proposed by using the soft values of each bit, which are related to the confidence levels when the physical layer demodulates and decodes the bit. The receiver has a better chance to obtain the correct bit by combining the soft values associated with the relay and the direct transmission paths, respectively. We then derive the bit error rate (BER) performance for the amplify and forward (AF) cooperative systems with soft value combining. Next, we extend the soft value combining for the demodulation and forward (DMF) cooperative systems, where the relay demodulates the received signal and forwards it to the destination in a separate modulation type, in order to further improve spectrum efficiency. Numerical results have validated the analysis, and demonstrated that the proposed soft value combining scheme is simple to implement and can achieve near optimal performance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".