PiPNC: Piggybacking Physical Layer Network Coding for multihop wireless networks
Bibliographic record
Abstract
This paper proposes a new relaying technique, Piggybacking Physical Layer Network Coding (PiPNC), which is most favorable in the scenario that two source nodes exchange data with the help of a relay, and the relay also needs to exchange data with one source node. PiPNC arranges transmissions in two stages, a multiple access (MA) stage and a broadcast (BC) stage, which compose one transmission cycle. In the MA stage, one of the source nodes (with a better channel quality to the relay) can piggyback the symbol targeting to the relay on the symbol targeting to the other source node. In the BC stage, the relay can piggyback the symbol targeting to the source node (with a better channel) on the broadcast symbol. In this way, two bidirectional information interchanges are achieved in one transmission cycle. Designs and optimizations of two PiPNC schemes are presented, and extensive simulations have been conducted to evaluate the system performance and identify the optimal location of the relay.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".