Joint Access and Resource Allocation in Ultradense mmWave NOMA Networks With Mobile Edge Computing
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Bibliographic record
Abstract
This article considers a two-tier heterogeneous network consisting of conventional sub-6-GHz macrocells along with millimeter-wave (mmWave) small cells, where mobile devices (MDs) can connect to either macrocell or small cells opportunistically via the nonorthogonal multiple access (NOMA) protocol. We employ the queuing theory in our network model to conduct an assessment on the execution delay, energy consumption and the total cost of offloading tasks in a mobile-edge computation offloading (MECO) system. The main goal is to design an energy-efficient MECO decision algorithm in an ultradense Internet of Thing (UD-IoT) network to analyze the tradeoff between execution delay and energy consumption. The proposed scheme jointly optimizes the communication and computation resource management, subject to the energy and delay constraints. Due to the mixed-integer nonlinear problem (MINLP) for resource allocation and computation offloading, an iterative algorithm along with the successive convex approximation (SCA) is proposed to achieve the optimum local frequency scheduling, power allocation, and computation offloading. The superior performance of the proposed MECO algorithm in our UD-IoT network is verified by the extensive numerical results.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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