Bibliographic record
Abstract
This paper studies coordinated multipoint (CoMP) transmission with non-orthogonal multiple access (NOMA) for a simultaneous wireless information and power transfer (SWIPT) network. We investigate two different CoMP-NOMA schemes, namely joint transmission-NOMA (JT-NOMA) and Alamouti NOMA (A-NOMA). Also, as the benchmark, joint transmission orthogonal multiple access (JT-OMA) is considered. Expressions are derived for the outage probabilities achieved by JT-NOMA, A-NOMA, and JT-OMA for cell edge users with quality of service (QoS) requirements. To compare these schemes in terms of spectral efficiency, we use the ε-outage rate region that is defined to be the set of all achievable rate pairs at which the outage probabilities are equal to ε. It is shown that A-NOMA is superior to both JT-NOMA and JT-OMA in high SNR. Also, it is analytically proved that JT-NOMA outperforms JT-OMA, when the circuit energy consumption is negligible. A particularly interesting observation is that when the circuit energy consumption is non-negligible, JT-NOMA is not always better than JT-OMA. It is shown that when the difference between the expectations of effective channel power gains is not sufficiently large, JT-OMA is superior to JT-NOMA for the same circuit energy consumption.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".