Dynamic Neural Network-Based Resource Management for Mobile Edge Computing in 6G Networks
Why this work is in the frame
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Bibliographic record
Abstract
Mobile edge computing (MEC) can be used to reduce the task delay for users with limited computing resources. However, in 6G networks, the diversity of tasks is greatly increased. For those extremely delay-sensitive small-size computing tasks, the inference delay of neural network (NN)-based algorithms such as resource allocation and task offloading cannot be ignored. As a hyperparameter, the inference cost of NN is usually difficult to adjust. Dynamic neural network (DyNN) is an emerging technique that improves the model efficiency by adjusting the network architecture on-demand according to the sample characteristics during inference. In this paper, we propose a DyNN-based resource management method for MEC that dynamically adjusts the depth and width of the NN according to the features of the task, improving computational efficiency and achieving a balance between inference delay and the management performance of computational and communication resources. Furthermore, to reduce the training cost of DyNN, a new training method is proposed in this paper, where all the blocks in DyNN are gradually trained in the order of size. Simulation results demonstrate that the proposed DyNN-based resource management method outperforms the traditional optimization algorithm and the static-NN-based method.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it