Energy Efficient Resource Management in SWIPT Enabled Heterogeneous Networks With NOMA
Why this work is in the frame
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Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) in heterogeneous network (HetNet) is a very promising scheme to meet the exponential growth of mobile data expected in the coming years. However, since wireless networks are becoming denser, the energy consumption of such networks is increasingly severe. Therefore, it is necessary to design novel energy efficiency (EE) maximization technologies under the constraint of limited energy supply. This paper investigates the resource optimization problem of NOMA heterogeneous small cell networks with simultaneous wireless information and power transfer (SWIPT). By decoupling subchannel allocation and power control, a low-complexity subchannel matching algorithm is designed. Furthermore, to maximize the energy efficiency, a power optimization algorithm is proposed using Langrangian duality. Aiming at the power allocation problem, the original non-convex and non-linear energy efficiency optimization problem is transformed into a more tractable one. Simulation results demonstrate the effectiveness and convergence of the proposed optimization scheme in terms of system energy efficiency.
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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 | 0.000 | 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