An Auction-Based Pareto-Optimal Strategy for Dynamic and Fair Allotment of Resources in Wireless Mobile Networks
Bibliographic record
Abstract
Ongoing advances in sophisticated mobile computing technologies and wireless communications have propelled substantial research work toward designing and implementing a new breed of mobile communications systems. One of the fundamental design objectives of such systems is to ensure complete roaming ability for the mobile users. In addition, upon handoff events, the mobile users also require to be able to perform renegotiations pertaining to their required quality-of-service (QoS) requirements with the concerned system. In this paper, we envision a fair and dynamic auction-based QoS negotiation scheme to deal with this issue. The envisioned scheme provides the mobile users with the flexibility to dynamically negotiate or renegotiate their preferred service levels with the corresponding service provider. The proposed technique has three crucial design objectives: First, it ensures a high level of fairness among the competing mobile users (each with a specific budget). Second, it ensures efficient utilization of the available network resources. Finally, its auction-based mechanism aims at maximizing the revenue of the service provider. A mathematical analysis is provided to demonstrate that, when the three design goals are taken into account, the resource allocation function that the proposed scheme provides represents a Pareto-optimal solution. The effectiveness of the proposed scheme is also verified through extensive simulations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".