Intelligent power optimization for capacity maximization in IRS-assisted NOMA networks
Bibliographic record
Abstract
This paper explores the performance of non-orthogonal multiple access (NOMA) in networks enhanced by intelligent reflecting surfaces, with a particular focus on optimizing power allocation to balance system capacity and user fairness. This article derives the optimal power allocation strategy and employs Karush–Kuhn–Tucker conditions to analytically solve the optimization problem. MATLAB simulations are used to validate the approach, ensuring both users meet their minimum data rate requirements under total power constraints. Results show that the optimal power allocation strategy prioritizes the weaker user (UE2) to ensure fairness while maintaining a sufficient capacity margin for the stronger user (UE1). As total power increases, the achievable capacity of UE1 grows logarithmically, reaching 19.41 bps/Hz at 20W, while UE2's capacity initially increases but saturates around 1.74 bps/Hz due to interference from UE1. Consequently, total system capacity exhibits diminishing returns, increasing from 19.122 bps/Hz at 5W to 21.132 bps/Hz at 20W. Further analysis reveals that as UE1's power increases, interference to UE2 rises, limiting its capacity growth. Despite this, optimization ensures both users meet their minimum capacity requirements, demonstrating the efficiency of NOMA in managing power distribution and interference.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".