Multi-Item Auction Based Mechanism for Mobile Data Offloading: A Robust Optimization Approach
Bibliographic record
Abstract
The opportunistic utilization of access devices to offload mobile data from cellular network has been considered as a promising approach to cope with the explosive growth of cellular traffic. To foster this opportunistic utilization, we consider a mobile data offloading market where mobile network operator (MNO) can sell bandwidth made available by the access points (APs) to increase MNO's profit. We formulate the offloading problem as a multi-item auction and study MNO's profit maximization problem. We discuss the conditions to (i) offload the maximum amount of data traffic, (ii) foster the participation of mobile subscribers (MSs) (individual rationality), (iii) prevent market manipulation (incentive compatibility) and (iv) preserve budget feasibility of MSs. Then, we propose a robust optimization based method to implement multi-item auction mechanism. We further propose two iterative algorithms that efficiently solve the offloading problem. The simulation results show the efficiency and robustness of our proposed methods for cellular data offloading.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".