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Record W2889455910 · doi:10.1109/ccece.2018.8447698

A Centralized Approach for Load Balancing in Heterogeneous Wireless Access Network

2018· article· en· W2889455910 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer networkComputer scienceLoad balancing (electrical power)Call blockingBlocking (statistics)Load managementWireless networkBandwidth (computing)Node (physics)WirelessDistributed computingHeterogeneous networkTelecommunicationsQuality of serviceEngineeringGrid

Abstract

fetched live from OpenAlex

Heterogeneous wireless access network (HWAN), composed of different radio access technologies (RATs) with overlapping zones, provides high data rates and supports bandwidth hungry application. In this paper, we explore a centralized approach for load balancing in HWAN that utilizes central controller node (CCN). The CCN balances the load by r-allocating the radio resources such that an equal load ratio is maintained across all the available RATs in HWAN. The performance of this centralized mechanism is evaluated through call blocking probability and network utilization. After load balancing, a decrease in call blocking probability for overload RATs and increase in BW utilization for underload RATs can be visualized from the obtained simulation graphs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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