Bi-Directional Multi-Hop Wireless Pipeline Using Physical-Layer Network Coding
Bibliographic record
Abstract
In this paper, the design of multi-hop physical layer network coding (PNC) is investigated. In the existing multi-hop PNC designs, the effects of error propagation and mutual-interference are not well addressed. Error propagation refers to that the estimation error at any node may propagate to the neighboring nodes, which may result in serious end-to-end bit errors. The impact of the mutual-interference from other transmitting nodes to a receiver determines the upper bound SINR of two neighboring nodes given end-to-end SNR. By carefully addressing these issues, we propose two multi-hop PNC designs, the direct multi-hop PNC (D-MPNC) and the stored multi-hop PNC (S-MPNC), where both designs achieve the throughput upper bound of one symbol per symbol duration, which is the same as that of the traditional PNC with a single relay. There is a tradeoff between the applications of D-MPNC and S-MPNC, which targets for simple-implementation and optimal end-to-end bit error rate (BER), respectively. We provide the detailed designs of D-MPNC and S-MPNC and obtain the end-to-end BER bounds theoretically. Extensive simulation results demonstrate the performance gain of the proposed multi-hop PNC compared with the traditional PNC in terms of end-to-end BER and end-to-end throughout.
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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 | 0.007 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".