Performance Evaluation of Downlink IRS and Uplink Cluster NOMA-Aided WPCN
Bibliographic record
Abstract
This paper studies the performance of integrated intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA)-aided wireless powered communication networks (WPCNs). The problem of maximizing the sum-throughput of the system under realistic operating conditions is formulated as a non-convex optimization problem. Given the complexity of jointly optimizing the decision variables for sum-throughput maximization, we propose a two-stage algorithm to achieve a solution. The algorithm first establishes an active DL energy beamforming matrix and IRS phase shift vector to maximize the sensors’ aggregated received power, followed by optimizing DL and UL time slots and UL energy resources. The numerical results demonstrate that the proposed algorithm increases the average sum-throughput by $45 \%$ in the perfect channel state information (CSI) scenarios. However, its performance declines with an increase in IRS elements under imperfect CSI, due to increased channel estimation errors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".