Exploiting Self-Information to Improve the Performance of Multi-Way Relay Channels
Bibliographic record
Abstract
Full data exchange with complex field network coding (CFNC) has been shown to achieve a throughput of 1/2 symbol per user per channel use (sym/U/CU) in a multiway relay channel (MWRC). Further, optimum precoding has been designed for a MWRC with a throughput of 1/2 sym/U/CU such that a rectangular quadrature amplitude modulation (QAM) constellation symbol is received. To achieve this throughput, any superimposed combination of user symbols must be distinguishable at the relay and users. In this paper, a decode-and-forward (DF) transmission scheme for full data exchange in a MWRC is presented which decreases the constellation size received by the users and hence leads to a downlink performance improvement. This is achieved by exploiting user self-information. It is also shown that the proposed DF transmission scheme leads to uplink performance improvement. The proposed transmission scheme is evaluated in both additive white Gaussian noise (AWGN) and Rayleigh 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".