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Record W2079624325 · doi:10.1109/tcomm.2015.2389833

Analyzing the Impact of Inter Cooperation Region Interference in Coordinated Multi-Point Uplink Networks

2015· article· en· W2079624325 on OpenAlexaff
S. Alireza Banani, Raviraj Adve

Bibliographic record

VenueIEEE Transactions on Communications · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTelecommunications linkErgodic theoryBase stationCoverage probabilityInterference (communication)Computer scienceCellular networkTopology (electrical circuits)Parametric statisticsStochastic geometryPoint (geometry)Mathematical optimizationLattice (music)MathematicsTelecommunicationsStatisticsPhysicsMathematical analysisCombinatorics

Abstract

fetched live from OpenAlex

We analyze the uplink of coordinated multi-point (CoMP) networks in which cooperation can be amongst N = 2 or N = 3 base stations (BSs). We consider a 2-D network of BSs on a regular hexagonal lattice wherein the cooperation tessellates the 2-D plane into cooperation regions (CRs); specifically, we analyze the impact of the interference between the CRs in the network. Our model accounts realistic propagation conditions, particularly including shadowing. We obtain accurate, closed-form, approximations for the user capacity coverage probability (CCP) and the ergodic capacity at each point within the CR. To provide a network-level analysis, we focus on the locations within each CR with the minimum CCP - “the worst-case point(s)”. The worst-case CCP and/or ergodic capacity can be used in parametric studies for network design. Here, the analysis is applied to obtain the relationship between cell size and CCP and, thereby, the required density of BSs to achieve a chosen target capacity coverage. The analysis also allows for a comparison between different orders of BS cooperation, quantifying the reduced required BS density from higher orders of cooperation. Comprehensive simulations are used to illustrate the accuracy of our analysis, including the approximations used for analytic tractability.

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: Methods · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.484

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 science0.0000.000
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 head0.056
GPT teacher head0.305
Teacher spread0.250 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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