Uplink Resource Allocation in Mobile Edge Computing-Based Heterogeneous Networks with Multi-Band RF Energy Harvesting
Bibliographic record
Abstract
Resource allocation in mobile edge computing (MEC)- based wireless networks with energy harvesting has attracted great attention. However, existing works assume that mobile devices are stationary while harvesting energy. In this paper, an uplink resource allocation strategy is developed in MEC-based heterogeneous networks. A random mobility model is designed to describe the movement of the user equipment (UE). Meanwhile, the UE can harvest energy from six frequency bands while moving along a certain path. The energy harvesting model of the UE is given by an integral expression. The objective of the resource allocation problem is to maximize the energy efficiency (EE) under the constraints of energy consumption, total data rate requirement, sub-carrier allocation, and transmission power. A quantum-behaved particle swarm optimization (QPSO) algorithm is employed to obtain a sub-optimal solution. Numerical results show that the amount of energy harvested by the UE decreases as the moving speed increases. Moreover, the QPSO algorithm has higher EE than an existing particle swarm optimization algorithm.
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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 | 0.001 |
| 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".