Distributed User Association in Energy Harvesting Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach
Bibliographic record
Abstract
The emerging ultra-dense small cell networks (UD-SCNs) will need to combat a variety of challenges. On the one hand, massive number of devices sharing the limited wireless resources renders centralized control mechanisms infeasible due to the excessive cost of information acquisition and computation. On the other hand, to reduce the energy consumption from fixed power grid and/or battery, network entities (e.g., small cell base stations and user devices) may need to rely on the energy harvested from the ambient environment (e.g., from environmental sources). However, opportunistic energy harvesting introduces uncertainty in the network operation. In this paper, we study the distributed user association problem for energy harvesting UD-SCNs. After reviewing the state-of-the-art research, we outline the major challenges that arise in the presence of energy harvesting due to the uncertainty (e.g., limited knowledge on energy harvesting process or channel profile) as well as limited computational capacities. Finally, we propose an approach based on the mean-field multi-armed bandit games to solve the uplink user association problem for energy harvesting devices in a UD-SCN in the presence of uncertainty.
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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 | 0.001 | 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 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".