Design and Analysis of Hierarchical Physical Layer Network Coding
Bibliographic record
Abstract
This paper proposes a new relaying technique, hierarchical physical layer network coding (H-PNC), aimed at increasing the spectrum and energy efficiency in multi-hop wireless networks by supporting two bi-directional traffic flows simultaneously. H-PNC is applicable to the scenario in which two source nodes exchange data with the help of a relay, and the relay also needs to exchange data with one source node in an asymmetric two-way relay channel network, where the channel conditions of two source-relay links are asymmetric. H-PNC arranges transmissions in two stages, the multiple access (MA) stage and the broadcast (BC) stage. In the MA stage, the source node with the better channel to the relay can superimpose the symbol targeting to the relay on the symbol targeting to the other source node. In the BC stage, the relay can superimpose the symbol targeting to the source node with the better channel on the broadcast symbol. Thus, one more bidirectional information exchange is achieved beyond traditional PNC. Designs and optimizations of three H-PNC schemes are presented, and the error performance of QPSK-BPSK H-PNC is derived. Extensive simulations have been conducted to evaluate the system performance. Both theoretical analysis and simulation results demonstrated that H-PNC achieves a substantial performance gain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".