Distributed convolutional-based coding for system non-idealities
Bibliographic record
Abstract
This paper proposes a coding scheme for cooperative networks in the non-ideal case with system imperfections where the source and relays share their antennas to create a virtual transmit array to transmit towards their destination. We focus on the problem of coding for the relay channels. While the relays may use several forwarding strategies, including amplify-and-forward (AF) and decode-and-forward (DF), we focus on coded DF relaying. In particular, we consider the case when all the nodes estimate the channel state information in the transmission process. We derive upper bounded expressions for the bit error rate (BER) assuming M-ary phase shift keying (M-PSK) transmission. It is shown that the performance is degraded due to the presence of channel estimation error. However, the observations made in ideal scenarios still hold for the non-ideal case. Also our analytical results have shown that a performance close to perfect channel knowledge can be obtained when the number of pilot symbols kpincreases. Also, with kppilot symbols, the performance was shown to approach the perfect channel knowledge case at high pilot to noise ratio (PNR) (Eb/N0). Finally, at low PNR, the proposed scheme cannot provide significant performance improvements.
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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 | 0.002 |
| 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 | 0.001 |
| 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".