Joint Design of Beamforming, Phase Shifting, and Power Allocation in a Multi‐cluster IRS‐NOMA Network
Bibliographic record
Abstract
The combination of non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) forms an efficient solution for significantly enhancing the energy efficiency of wireless communication systems. This chapter focuses on a downlink multi-cluster NOMA network, with each cluster being supported by an IRS. The objective is to minimize transmit power by optimizing beamforming, power allocation, and phase shifts for each IRS. The formulated problem is nonconvex and presents challenges due to the interconnected variables: the beamforming vector, power allocation coefficients, and phase shift matrix. To address this intricate problem, an alternating optimization-based algorithm is proposed. This algorithm iteratively solves the transmit power optimization problem. Simulation results demonstrate that the suggested alternating algorithm outperforms the partial exhaustive search algorithm, OMA-IRS, and NOMA with random IRS phase shifts in terms of performance improvement.
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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".