Outage Analysis of NOMA-Enabled Backscatter Communications With Intelligent Reflecting Surfaces
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Bibliographic record
Abstract
Intelligent reflecting surface (IRS) has emerged as a potential technology to achieve smart wireless communications and high energy efficiency. On the other hand, nonorthogonal multiple access (NOMA)-enabled backscatter communications have shown a great potential in large-scale Internet of Things (IoT) networks. In this article, we consider a downlink IRS-assisted backscatter communication with NOMA. We further consider a two-user scenario with channel disparity from the base station. We first derive the probability density function of the sum of the modulus of reflected channels, where each channel follows the Rayleigh distribution with dissimilar variances. The respective and generalized closed-form outage probability expressions are derived for the considered scenario. Simulation results validate the accuracy of the analytical outage probability expressions. We demonstrate that the far user can achieve a superior performance with the increase of reflecting elements or the reflection coefficients.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it