Joint Phase Shift and Beamforming Design in a Multi-User MISO STAR-RIS Assisted Downlink NOMA Network
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Bibliographic record
Abstract
Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) has gradually been considered as a promising technology in the wireless communication networks. Besides, non-orthogonal multiple access (NOMA) is also the key technology in the sixth-generation (6G) wireless communication system. In this work, we study a multiple input single output (MISO) STAR-RIS assisted NOMA downlink network and investigate the energy efficiency (EE) maximization to achieve the tradeoff between the sum rate and the power consumption. The original formulated problem is non-convex due to the coupled beamforming vectors of the users and phase shifts of the STAR-RIS. To efficiently solve the problem, we split the original non-convex problem into the phase shift and beamforming optimization problems and then solve them alternatively. In the phase shift optimization, fractional programming (FP) is applied to transform the sum rate maximum problem to convex semidefinite relaxation (SDR) one with the rank-one constraint. After this, a novel sequential rank-one constraint relaxation (SROCR) is proposed to convert the rank-one constraint into a convex one, which can effectively overcome the inadequacy of Gaussian randomization, i.e., quality of the solutions and computational complexity. Similarly, FP is applied to solve the beamforming problem by transforming it to SDR problem. It turns out that the optimal solution of the SDR beamforming optimization problem can be guaranteed to be rank-one by the mathematical proof and experiments. The simulation results demonstrate the STAR-RIS NOMA system can achieve the superior performance in EE.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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