<title>Managing pricing of QoS for service differentiation in wireless networks</title>
Bibliographic record
Abstract
In this paper we consider service differentiation in wireless networks using pricing policy. The pricing methodology examined is based on "auctioning" pricing. In the present work, it is applied on IEEE 802.11-based wireless networks. The uplink traffic in an IEEE 802.11 wireless network operating in the PCF mode is considered to be "thin" as the mobile users in the network are subject to power limitations and the biggest volume of traffic occurs on the downlink - from the Base Station to the mobile users. The Base station advertises different levels of Quality of Service (QoS) and the Mobile Users are competing for channel resources by bid requests. Based on the variability of the wireless channel, the amount of the available bandwidth shared between the mobile users changes, the price of the QoS changes as well and the users might change the bids they are offering or change the QoS they request from the Base Station. In the present bidding and pricing scheme, an analytical model for price determination is provided and the results of OPNET based simulations for prove of concept are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".