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Record W2168241011 · doi:10.1109/iscc.2006.46

Capacity Enhancement in CDMA Cellular Networks using Multi-hop Communication

2006· article· en· W2168241011 on OpenAlexaff
Ayman Radwan, Hossam S. Hassanein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsQueen's University
Fundersnot available
KeywordsCellular networkTelecommunications linkComputer scienceInterference (communication)Base stationComputer networkCode division multiple accessNear-far problemHop (telecommunications)Spread spectrumElectronic engineeringEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

The Multi-hop CDMA cellular concept has been proposed to overcome cellular drawbacks, like congestion and load imbalance. Although it is a widely accepted that multi-hopping increases cellular capacity, it has never been quantified. In this paper, the capacity increase in multi-hop CDMA cellular networks is quantified. To this end, and since CDMA networks are interference limited, we derive equations for interference in multi-hop cellular networks at base stations (BSs) and relaying mobile terminals (MTs) in the uplink. The interference formulas are used to verify that capacity can be increased, by increasing either the number of simultaneous calls or data rate. A 10% increase in the number of simultaneous calls is shown to be possible even under worst-case scenarios. This increase is achievable while keeping interference at relaying MTs below acceptable thresholds. The novelty of this paper is that it quantifies, and for the first time, interference levels at MTs and BSs as well as potential capacity enhancements.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.469

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 communication0.0000.000
Open science0.0010.000
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 head0.069
GPT teacher head0.282
Teacher spread0.212 · 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
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

Citations21
Published2006
Admission routes1
Has abstractyes

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