Device-to-Device Aided Cooperative NOMA Transmission Exploiting Overheard Signal
Bibliographic record
Abstract
A novel device-to-device (D2D) aided cooperative non-orthogonal multiple access (NOMA) scheme (termed as D2D-SG-NOMA) is proposed, where two similar gain (SG) near users (NUs) with the capability of D2D communication and one far user (FU) are served within two time slots. The NOMA pair is formed with a NU and the FU. The paired NU is employed as a decode-and-forward relay to assist FU. Contrarily, the unpaired NU can receive signals simultaneously from the base station (BS) and the other NU during the second time slot. Two different scenarios (i.e., S1and S2) are investigated insightfully. In S1, the direct link between the BS and FU does not exist, whereas the direct link between the BS and FU exists in S2. The delay-tolerant capacity (DTC), outage probability, diversity order, and delay-limited capacity are investigated along with analytical formulation. The D2D-SG-NOMA achieves an increase of around 66% and 85% in DTC at 0 dB signal-to-noise (SNR) under S1and S2, respectively than the existing NOMA scheme with successive relaying (termed as SR-NOMA). Contrarily, a reduced DTC improvement (i.e., around 3% and 10% in S1and S2, respectively) is obtained at 40 dB SNR due to increased inter-symbol interference.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".