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Record W1520774949 · doi:10.5772/14772

Mobility and QoS-Aware Service Management for Cellular Networks

2011· book-chapter· en· W1520774949 on OpenAlexaff
Omneya Issa

Bibliographic record

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsQuality of serviceMobile QoSComputer networkComputer scienceMobility managementService (business)BusinessService delivery frameworkMarketing

Abstract

fetched live from OpenAlex

As the technologies have evolved in cellular systems from 1G to 4G, the 4G system will contain all the standards that earlier generations have implemented.It is expected to provide a comprehensive packet-based solution where multimedia applications and services can be delivered to the subscriber on an anytime, anywhere basis with a satisfactory enough data rate and advanced features, such as, quality of service (QoS), low latency, high mobility, etc.Nevertheless, the 4G cellular system remains a wireless mobile environment, where resources are not given and their availability is prone to dynamic changes.Hence, the basis for QoS provisioning is to control the admission of new and handoff subscriber services in such a way to avoid future detriment perturbation of already connected ones.This task becomes a real challenge when service providers try to raise their profit, by maximizing the number of connected subscribers, while meeting their customer QoS requirements.The problem can be summarized in that the cellular network should meet the service requirements of connected users using its underlying resources and features.These resources must be managed in order to fulfill the QoS requirements of service connections while maximizing the number of admitted subscribers.Furthermore, the solution(s) must account for the environmental and mobility issues that influence the quality of RF channels, such as, fading and interference.This is the role of service management in cellular networks.In this chapter, we address service admission control and adaptation, which are the key techniques of service management in mobile cellular networks characterized by restricted resources and bandwidth fluctuation.Several research efforts have been done for access control on wireless networks.The authors of (Kelif & Coupechoux, 2009) developped an analytical study of mobility in cellular networks and its impact on quality of service and outage probability.In (Kumar & Nanda, 1999), the authors have proposed a burst-mode packet access scheme in which high data rates are assigned to mobiles for short burst durations, based on load and interference measurements.It covers burst-mode only assuming that mobiles have only right to one service.The authors of (Comaniciu et al., 2000) have proposed an admission control for an integrated voice/www sessions CDMA system based on average load measurements.It assumes that all data users have the same bit error rate (BER) requirements.A single cell environment is modeled and no interference is considered.In (Kwon et al., 2003), authors have presented a QoS provisioning framework where a distributed admission control algorithm guarantees the upper bound of a redefined QoS parameter called cell overload probability.Only a single Mobility and QoS-Aware Service Management for Cellular Networks

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0030.003
Research integrity0.0000.001
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 head0.051
GPT teacher head0.268
Teacher spread0.217 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2011
Admission routes1
Has abstractyes

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