On the Performance of End-to-End Cooperative NOMA-Based IoT Networks With Wireless Energy Harvesting
Bibliographic record
Abstract
This article studies the end-to-end uplink (UL) and downlink (DL)-outage probability (OP) of an Internet of Things (IoT) network with radio-frequency (RF) energy harvesting (EH) over Nakagami-m fading channels. Power-domain nonorthogonal multiple access (NOMA) is adopted to support both the UL and DL transmissions to increase the network spectral efficiency. The system end-to-end UL and DL outage probabilities are analyzed where exact closed-form expressions are derived. The system performance is explored for various system parameters, such as time allocation for EH, transmission power, fading conditions, number of IoT devices (IoDs), and data rate. The obtained analytical results are corroborated using Monte Carlo simulation for various operating scenarios. The obtained results show that the optimum harvesting time may broadly vary based on the adopted system parameters. Moreover, the results show that the system OP is highly sensitive to the harvesting time where OP may vary drastically if the harvesting time deviates from the optimum. The impact of the perfect successive interference cancelation (SIC) (PSIC) is also evaluated and compared with imperfect SIC (ISIC), and the obtained results show that the OP of the system can be significantly underestimated with the PSIC assumption. Therefore, the commonly used PSIC assumption may cause substantial deviation from the practical case where the detection process experiences ISIC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".