A partial coherent detector for orthogonal modulations in two-way relay communications with physical network coding and fading
Bibliographic record
Abstract
We present in this paper a partial coherent receiver for detecting (at the relay) the modulo-2 sum bit of the uplink orthogonal modulations in a two-way relay communication system with physical network coding and fading. The detector exploits the availability of implicit pilot symbols in every signaling interval of an orthogonal modulation and is able to provide reasonably accurate data recovery without the need of sending pilot symbols. Using the characteristic function approach, we were able to derive a tight analytical upper bound on the bit-error-rate (BER) of the detector. The first component in the upper bound tells us that when the uplink symbols from the two users are identical, the BER approaches that of conventional point-to-point coherent detection, i.e. the multiple-access interference arising from physical network coding has no effect on the BER when the two data bits are identical. On the other hand, the second component in the upper bound shows that when the data from the two users are different, the BER increases dramatically. In essence, the BER of the proposed partial coherent detector is the average of those of the coherent and non-coherent detectors. We also outline in the paper a simple technique for achieving full coherent detection at the relay without resorting to transmitting any pilot symbols.
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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) | 0.001 | 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".