HePNC: Design of physical layer network coding with heterogeneous modulations
Bibliographic record
Abstract
Physical layer network coding (PNC) has been proposed for the two-way-relay scenario. The existing PNC solutions typically use the same modulation for the source nodes' signals which may not be desirable for practical situations when the amount of data exchanged between the two source nodes are un-equal and their links to the relay node are heterogeneous. In this paper, physical layer network coding with heterogeneous modulations (HePNC) has been proposed to further improve the spectrum and energy efficiency considering the above mentioned heterogeneity. Similar to the existing PNC, HePNC also includes two stages: the multiple access (MA) stage for two source nodes to transmit data to the relay and the broadcast (BC) stage for the relay to broadcast data to both source nodes. The main difference is that at the MA stage, HePNC can select heterogeneous modulation methods according to the channel conditions and the ratio of data to be exchanged. We present two sample designs of HePNC, including QPSK-BPSK and 8PSK-BPSK, and obtain the mapping rules for the relay's de-noising and forwarding based on the neighbor clustering algorithm. In addition, we discuss the end-to-end bit-error-rate, energy efficiency and optimal relay location selection when HePNC is used. Extensive simulations demonstrated that the proposed HePNC can substantially enhance the end-to-end throughput and the energy efficiency compared to the homogeneous PNC, and thus it is a promising technology for future green communication systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".