Performance Analyses of SWIPT-NOMA Enabled IoT Relay Networks
Bibliographic record
Abstract
In this paper, the performance of Non-Orthogonal multiple access (NOMA) with Simultaneous Wireless Information and Power Transfer (SWIPT) for Internet of Things (IoT) relaying networks over Nalagami-m fading channel is investigated. Comparison with benchmark schemes using Time Division Multiple Access (TDMA) Orthogonal Multiple Access (OMA) with SWIPT is done. Closed-form expressions for the outage probability of all users, the system throughput, and the cooperative relaying between users are analytically derived and validated by Monte Carlo simulations in Nakagami-m fading channel. It is analytically proven that the SWIPT-NOMA outperforms the SWIPT-OMA schemes, reducing the outage probability and enhancing the overall system throughput with the appropriate choice of power allocation and user target rates. With cooperative communication from the near user and the relay to the far user, a reduction of the outage probability and an enhancement of the diversity gain for the far user of the SWIPT-NOMA were proven to be achieved even if one of these two links was in an outage.
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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 | 0.004 |
| 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 | 0.001 | 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".