Superposition Coding in Alternate DF Relaying Systems with Virtual MIMO IRI Cancellation
Bibliographic record
Abstract
This paper considers a layered transmission between the source and the destination aided by two half-duplex relays. In the proposed system, the single antenna source continuously broadcasts superposition coded (SC) base and enhancement layers, while relays, with one active antenna, retransmit in turn the enhancement layer. Using two antennas, the destination as well as the relays receive mixed base, enhancement, and delayed enhancement layers through over-the-air signal summation. To recover from inter-relay interference (IRI), the receiving relay decouples two spatial streams representing data of interest using the virtual Multiple Input Multiple Output (MIMO) channel from the source and the transmitting relay. Because of the network topology, the receiver deploys both virtual MIMO and Successive Interference Cancellation (SIC) techniques to first decode the delayed enhancement layer and then the delayed base layer. In this decode-and-forward (DF) transmission scheme, the transmissions from the source and the relays are aligned in time, power, and spatial domains to optimize the system throughput and reliability. Finally, this paper demonstrates that the SC with alternate relaying in the proposed single input multiple output (SIMO) setup has a better performance than similar conventional 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 | 0.001 |
| 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".