Superposition coding in alternate DF relaying systems with inter-relay interference cancellation
Bibliographic record
Abstract
This paper considers the layered transmission between the source and the destination over two relays using superposition coding (SC). In this two-hop decode-and-forward (DF) relaying system, because of the network topology, the receiver continuously decodes only the base layer of the direct transmission from the source; the enhancement layer is decoded with a delay based on the transmission from one of the two relays sending and receiving in alternate time slots. Alternate relaying improves the spectral efficiency of the scheme, however it causes inter-relay interference (IRI), which may limit the system performance if not mitigated. To allow the proper data recovery at the relays and at the destination, we have designed successive interference cancellation decoding schemes that consider different power allocations to the base and enhancement layers. Specifically, based on the channel gains between different transmitter-receiver pairs, the transmissions from the source and the relays are aligned in time and power as to optimize the system throughput. Finally, based on the analytical derivations and simulations, we present numerical results for capacity improvements in the proposed scheme over the existing schemes.
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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) | 0.001 | 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 | 0.001 | 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".