HePNC: A Cross-Layer Design for MIMO Networks with Asymmetric Two-Way Relay Channel
Bibliographic record
Abstract
Traditional communication system typically separates the configuration of the physical layer from the network traffic load and topology. In this paper, we study how to apply physical-layer network coding considering the locations and traffic loads of multiple nodes in multiple-input multiple-output (MIMO) networks. We propose the heterogeneous-modulation physical-layer network coding (HePNC) design for MIMO networks with asymmetric two- way relay channel (TWRC), where all nodes are equipped with multiple antennas. Comparing to the single-antenna case, we study how to ensure the goodput with a fixed per-bit-energy can be scaled up w.r.t. the number of antennas, and also achieve performance gains in terms of end-to-end bit error rate (BER). The MIMO HePNC transmission includes the multiple access (MA) and broadcast (BC) stages. As the global channel state information (CSI) may be too costly to obtain, we propose two practical MIMO HePNC protocols based on maximum likelihood (ML) multi-user detector (MUD) that do not rely on global CSI. The first protocol is a heuristic one evolved from the single-input single-output (SISO) HePNC, and the second protocol upgrades the design and performance of both of the MA and BC stages. Analytical and extensive simulations demonstrated that, with two antennas each, the proposed MIMO HePNC protocols can not only double the goodput, but also achieve a substantial reduction on error rate, which indicates that combining HePNC and MIMO is a very promising cross-layer solution. We further discuss the impacts of the bottleneck link and provide guidelines on the relay location selection.
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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 | 0.000 |
| 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".