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Exploiting Mobility Diversity in Sharing Wireless Access: A Game Theoretic Approach

2010· article· en· W2160543884 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Manitoba
FundersNanyang Technological UniversityUniversity of Manitoba
KeywordsComputer scienceComputer networkWirelessQuality of serviceChannel (broadcasting)Wireless networkReservationGame theoryNetwork packetWireless distribution systemWi-Fi arrayTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

We propose a wireless access scheme which is based on a channel reservation sharing method for a group of mobile users. This proposed scheme exploits the mobility diversity of the mobile users in order to reduce the cost of wireless access. Another aspect of the proposed scheme is contention resolution among mobile users belonging to the same group in order to access the reserved channel while they are at the same location. A game theoretic model is developed for this wireless access scheme through which the rational mobile users can minimize the cost of wireless access while satisfying their quality-of-service (QoS) requirements (e.g., packet loss rate and average packet waiting time). The proposed game model consists of two interrelated formulations: a coalitional game for channel reservation and a stochastic game for channel access. The stable coalitional structure and equilibrium channel access policy are obtained from this game model.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.570
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.268
Teacher spread0.231 · 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