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Record W4238046099 · doi:10.32920/ryerson.14646510

An SMDP-Based initial radio access technology selection method for heterogeneous wireless networks

2021· preprint· en· W4238046099 on OpenAlexaff
Kazi R Islam

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHeterogeneous networkComputer scienceComputer networkHeterogeneous wireless networkRadio access technologyWireless networkSelection (genetic algorithm)WirelessEnergy consumptionMarkov decision processAccess networkService (business)Markov processDistributed computingBase stationTelecommunicationsEngineeringUser equipmentArtificial intelligence

Abstract

fetched live from OpenAlex

Heterogeneous Wireless Networks (HetNets) are a class of networks that have been designed to provide an open and flexible architecture for the coexistence of various distinct Radio Access Technologies (RATs). In such networks, one of the key challenges is the selection of the best available RATs when a user is connected upon making an incoming service request. This thesis proposes a Joint Call Admission Control (JCAC) - based approach for initial RAT selection in HetNets composed of two co-located wireless networks . The RAT selection problem in such HetNet is formulated by using a JCAC-based Semi-Markov Decision Process (SMDP) model, where the JCAC policy is determined according to a network cost function that weights three parameters: a cost of blocking the incoming service request, a cost of accessing RAT, and the RAT energy consumption cost. Simulation results are provided, showing the benefits of our approach in terms of RAT selection and resulting network energy consumption cost .

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.839
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
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 head0.019
GPT teacher head0.318
Teacher spread0.299 · 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 teacher head, not a consensus.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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