Design and Analysis of Heterogeneous Physical Layer Network Coding
Bibliographic record
Abstract
In this paper, physical layer network coding with heterogeneous modulations (HePNC) is proposed for the asymmetric two-way relay channel (TWRC) scenario. The existing PNC solutions using the same modulation for signals transmitted from two source nodes may not be desirable for practical situations when traffic loads exchanged between the sources are unequal and channel conditions of source-relay links are heterogeneous. HePNC includes two stages: multiple access (MA) and broadcast (BC) stages. In the MA stage, the two source nodes transmit to the relay simultaneously with heterogeneous modulations selected according to the channel conditions and the ratio of traffic loads exchanged between the sources, and then the signals superimposed at the relay are mapped to a network-coded symbol by a mapping function adaptively; in the BC stage, the relay broadcasts the network-coded symbol back to both sources with a modulation selected according to the bottleneck link's channel condition. We present three HePNC designs, including QPSK-BPSK, 8PSK-BPSK and 16QAM-BPSK HePNC. How to design and optimize the mapping function is investigated and the error performance of QPSK-BPSK HePNC is analyzed. We further study the HePNC system performance, throughput upper bound and energy efficiency. Extensive simulations demonstrated that the proposed HePNC can substantially enhance the throughput and energy efficiency compared with the existing PNC.
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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 | 0.000 | 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".