Energy Efficient Power Domain Non-Orthogonal Multiple Access Based Cellular Device-to-Device Communication for 5G Networks
Bibliographic record
Abstract
This paper examines the resource block and power allocation in the power domain non-orthogonal multiple access (PD-NOMA) based cellular device-to-device (D2D) systems. To improve the energy efficiency of the D2D systems and to manage the mutual interference level as well as the quality of service (QoS) requirement of cellular users, different power level is applied to the D2D users sharing the same resource blocks (RBs) to the legacy users. It is essential to design an efficient resource block and power allocation method for PD-NOMA based cellular D2D systems which guarantee the successive interference cancellation (SIC) order in the power allocation solution. In this paper, we propose an iterative algorithm of resource block and power allocation for cellular D2D system which incorporates the SIC aware geometric water filling (GWF) method in the power allocation solution. It is shown that the proposed SIC aware geometric water filling achieves higher energy efficiency compared to iterative water-filling (IWF) power allocation and the GWF based orthogonal multiple access (OMA) method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".