Equilibriums in the Mobile-Virtual-Network-Operator-Oriented Data Offloading
Bibliographic record
Abstract
Cellular networks are now facing severe traffic overload problems due to the explosive growth of mobile data traffic. One of the promising solutions is to offload part of the traffic through WiFi. In this paper, we investigate an oligopoly offloading market, where several mobile virtual network operators (MVNOs) compete to serve end users using network infrastructures leased from the host mobile network operator (MNO). First, we study the competitive interaction among multiple MVNOs considering the overload problems of the offloading market. Then, we formulate the interaction as a noncooperative inventory game, where each MVNO determines the amount of cellular traffic provided to end users (named as the traffic inventory). Particularly, we investigate two different behavior patterns of the MVNOs known as Cournot and Stackelberg models. Then, we analyze and derive the existence and uniqueness of the equilibrium in each inventory game. Furthermore, algorithms are designed to achieve the equilibriums. Based on these analyses, we find the optimal inventory strategy for these competing MVNOs. Finally, simulation results demonstrate the interactions among the MNO, MVNOs, and end users in the offloading market.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".