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Record W1995794661 · doi:10.1109/iscc.2007.4381541

On the Capacity of Multi-hop CDMA Cellular Networks

2007· article· en· W1995794661 on OpenAlexaff
Ayman Radwan, Hossam S. Hassanein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsHop (telecommunications)Interference (communication)Computer networkComputer scienceCellular networkTelecommunicationsSpread spectrumChannel capacityCode division multiple access

Abstract

fetched live from OpenAlex

The capacity of CDMA cellular networks is interference limited. Multi-hop communication promises to reduce interference, hence increasing capacity. However, such capacity gains depend on the actual interference. The location of a call determines its interference effect on the network. In this paper, we study the effect of call distribution on the capacity of multi-hop CDMA cellular networks. The capacity of multi-hop case is compared to that of single hop case. The effect of non-uniform call distribution is studied. It is shown that in the multi-hop case call distribution in a cell affects the capacity of this cell but hardly the capacity of its neighboring cells. The case is reversed in the single-hop case. Call distribution in a cell has no effect on the capacity of this cell, but it can have a significant effect on the capacity of surrounding cells. It is shown that if calls tend to originate near the border of one cell, this can seriously degrade the capacity of the whole network. This scenario is alleviated in the multi-hop case due to the shorter distances signals have to travel, resulting in lower interference. This paper also highlights scenarios where multi-hop communication is deeply needed.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.063
GPT teacher head0.290
Teacher spread0.227 · 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

Citations3
Published2007
Admission routes1
Has abstractyes

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