Performance Analysis of Cooperative NOMA with Dynamic Decode-and-Forward Relaying
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) is a promising multiple access technique, which exploits the power domain to enhance the spectral efficiency of the fifth generation (5G) wireless networks. In this paper, we propose a dynamic decode-and-forward (DDF) based cooperative NOMA scheme for downlink transmission to enhance the reception reliability of spatially random users. In DDF-based cooperative NOMA, the user closer to the base station decodes the superimposed mixture of the users' signals received from the base station based on partial reception, and then forwards the signal intended for the far user. To avoid the need for instantaneous channel state information at the base station, we consider random user pairing, where the users are randomly paired for NOMA transmission. Tools from point process theory are utilized to derive the outage probability of the proposed DDF-based cooperative NOMA scheme. Simulation results validate the performance analysis and demonstrate the performance gains of the proposed DDF-based cooperative NOMA scheme over conventional NOMA and cooperative NOMA.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".