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Record W2063476153 · doi:10.1109/mcom.2007.358861

WIRELESS BROADBAND ACCESS: WIMAX AND BEYOND - Integration of WiMAX and WiFi: Optimal Pricing for Bandwidth Sharing

2007· article· en· W2063476153 on OpenAlexaff
Dusit Niyato, Ekram Hossain

Bibliographic record

VenueIEEE Communications Magazine · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsWiMAXComputer networkComputer scienceWireless broadbandInternet accessBackhaul (telecommunications)Bandwidth (computing)Mobile broadbandQuality of serviceTelecommunicationsBroadbandWireless networkWirelessThe InternetBase station

Abstract

fetched live from OpenAlex

Broadband wireless access networks based on WiMAX can provide backhaul support for mobile WiFi hotspots. We consider an integrated WiMAX/WiFi network for such an application where the licensed WiMAX spectrum is shared by the WiFi access points/routers to provide Internet connectivity to mobile WiFi users. The WiMAX backbone network and WiFi hotspots are operated by different service providers. Issues such as protocol adaptation, quality of service support, and pricing for bandwidth sharing that are related to integration of these networks are discussed. In addition, we propose a model for optimal pricing for bandwidth sharing in an integrated WiMAX/WiFi network. A Stackelberg leader-follower game is formulated to obtain the optimal pricing solution for bandwidth sharing. Performance evaluation results reveal some interesting insights into the problem

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.026
GPT teacher head0.292
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations162
Published2007
Admission routes1
Has abstractyes

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