Exploiting Antenna Diversity to Enhance Hybrid Cooperative Non-Orthogonal Multiple Access
Bibliographic record
Abstract
Cooperative non-orthogonal multiple access (C-NOMA) is a novel multiple access technology that is considered as a promising solution for 5G and beyond. The technique has been proposed as a combination between NOMA and cooperative communications, such as device-to-device (D2D) communications. In this letter, we will address some limitations of the state-of-the-art C-NOMA model and promote an enhancement to the contemporary version. The improvement is based on an exhaustive exploitation of the antennas mounted at the users devices. Based on this, we revisit the rate analysis and the performance optimization of C-NOMA systems. Simulation results reveal the robustness of the proposed scheme in the presence of high self-interference (SI) and insightful comparisons with other previously proposed schemes in the literature are provided.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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".