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Record W2111123973 · doi:10.1109/vetecf.2000.886817

Performance optimization of single frequency broadcast systems in FDD-CDMA cellular bands for wireless multimedia services

2002· article· en· W2111123973 on OpenAlexaff
W. Wong, E.S. Sousa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer networkCellular networkInterference (communication)Code division multiple accessFrequency allocationChannel allocation schemesChannel (broadcasting)Electronic engineeringFrequency bandWirelessRadio spectrumWireless networkSignal-to-noise ratio (imaging)Stochastic geometryTelecommunicationsEngineeringBandwidth (computing)

Abstract

fetched live from OpenAlex

We study performance optimization of single frequency broadcast systems (SFBSs) spectrally underlaid in an FDD-CDMA cellular network which operates in a high frequency band (HFB) for the forward link and a low frequency band (LFB) for the reverse link. Interference scenarios between the cellular network and the SFBSs in both HFB and LFB are identified, and an interference analysis is presented to evaluate the received bit-energy-to-noise ratio (E/sub b//N/sub o/). We also study a family of frequency allocation strategies for use by the SFBSs: (1) fixed channel allocation (FCA), (2) random channel allocation (RCA), and (3) distance based channel allocation (DBCA). The simulation results show that, in order to maximize the outage performance of the FDD-CDMA cellular network, and in the meantime to keep the outage performance of the SFBSs at an acceptable level, the DBCA with HFB as the inner tier and LFB as the outer tier should be adopted by the SFBSs given that D is properly adjusted. A good choice of D is to set D=0.76 at which acceptable outage performances of both FDD-CDMA cellular network and SFBSs can be achieved.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.033
GPT teacher head0.241
Teacher spread0.208 · 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.

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

Citations1
Published2002
Admission routes1
Has abstractyes

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