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Record W2135887976 · doi:10.1109/wimob.2006.1696347

Integrating UMTS and Mobile Ad Hoc Networks

2006· article· en· W2135887976 on OpenAlexaff
Jade Wu, Muhammad Jaseemuddin, Amir Esmailpour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer networkComputer scienceGeneral Packet Radio ServiceUMTS frequency bandsWireless ad hoc networkMobile ad hoc networkInternetworkingVehicular ad hoc networkHandoverGPRS core networkNetwork packetWirelessTelecommunicationsThe Internet

Abstract

fetched live from OpenAlex

Although cellular networks such as GPRS (2.5G) or UMTS (3G) have achieved both circuit switching and packet switching services with wide area coverage, they still fall short of meeting the high data rate demands of mobile users. Integrating cellular network with other high-speed and low-cost wireless technologies is a key challenge for migrating to 4G. The IEEE 802.11 technology that has been well-developed and widely used in local area networks seems to be a proper choice for fulfilling users' expectations in hotspots. Its ad hoc mode of operation allows mobiles users to achieve connectivity with low infrastructure support. This paper proposes a design to achieve the internetworking between 802.11 mobile ad hoc network (MANET) and UMTS. The key approach in our design is to model inter-system handover as inter-SGSN handover, and connect gateway in the ad hoc network with the GGSN

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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