Energy efficient resource allocation for NOMA in cellular IoT with energy harvesting
Bibliographic record
Abstract
The Internet of Things (IoT) offers connectivity of massive low-power devices and sensors through the Internet which requires spectrum and energy efficient solutions. Recently, non-orthogonal multiple access (NOMA) has been investigated to address the challenges associated with spectral efficiency and dense deployment of a large number of devices in 5G cellular networks. Further, energy harvesting can enhance the energy efficiency of IoT devices. In this paper, we propose an energy-efficient resource allocation scheme for NOMA (EERA-NOMA) in cellular IoT with RF energy harvesting to address the above mentioned challenges. We model a framework to optimize user grouping of IoT devices in most appropriate resource blocks, power allocation, and time allocation for information transfer and energy harvesting. The objective is to maximize energy efficiency while satisfying constraints on the minimum data rate requirement of each user and transmit power. We adopted mesh adaptive direct search (MADS) algorithm to solve the formulated problem. Simulation results are presented to show the performance of proposed framework in comparison with existing work.
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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".