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Record W2096447960 · doi:10.1109/glocom.1997.638461

Capacity analysis of SFHMA cellular systems under a unified outage criterion

2002· article· en· W2096447960 on OpenAlexaff
J. Zhuang, M.E. Rollins, Jing‐Fang Huang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsTrunkingBlocking (statistics)Computer scienceInterference (communication)Probabilistic logicCellular radioOutage probabilityCommunications systemTopology (electrical circuits)Computer networkRadio resource managementTelecommunicationsFadingBase stationWirelessEngineeringElectrical engineeringWireless networkDecoding methods

Abstract

fetched live from OpenAlex

Slow frequency hopping (SFH) has been proposed as a candidate for multiple access (MA) in cellular radio. In this system, users hop independently over a set of carriers according to a probabilistic law. Interference originates from colliding users that hop simultaneously to the same carrier. In this paper, a new method for evaluating the capacity of SFHMA cellular radio systems is presented. Collision statistics are derived using well-known trunking theory models, and used to obtain the interference statistics as a function of average cell loading. A new definition of outage probability is proposed that integrates the hard blocking nature of slotted systems and the soft blocking characteristic of interference-limited systems into a single measure of outage performance. Using this framework, capacity may be evaluated in a unified manner for SFHMA systems with varying numbers of carriers and degrees of frequency re-use.

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)0.007
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.276
Teacher spread0.184 · 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
GenreMethods

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
Published2002
Admission routes1
Has abstractyes

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