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Record W2889009405 · doi:10.1109/ccece.2018.8447574

Inter-Cell Interference Coordination Using Fractional Frequency Reuse Scheme in Multi-Relay Multi-Cell OFDMA Systems

2018· article· en· W2889009405 on OpenAlexaff
Ali M. Saleh, Ngon Thanh Le, A.B. Sesay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOrthogonal frequency-division multiple accessInterference (communication)Orthogonal frequency-division multiplexingFrequency reuseCellular networkComputer scienceRelayElectronic engineeringFrequency-division multiple accessReuseComputer networkEngineeringBase stationChannel (broadcasting)

Abstract

fetched live from OpenAlex

Inter-Cell Interference Coordination (ICIC) using frequency reuse schemes in Orthogonal Frequency Division Multiple Access (OFDMA) cellular networks is one of the most promising approaches to reduce the effect of interference and to improve the system performance. Fractional Frequency Reuse (FFR) schemes are efficient interference mitigation techniques that have been used to improve system performance in multirelay multi-cell OFDMA cellular networks, especially for the cell edge users. The purpose of FFR design is to deploy frequency patterns (sets) in such a way that a Mobile Station (MS) user can reduce interference from adjacent cells. The Frequency Reuse Factor (FRF) of 7/3 with frequency reuse pattern (7, 3, 1) is used to improve the system performance of FRF=1 and FRF=3. This paper proposes a new frequency pattern and deploys frequency sets with Amplify and Forward (AF) fixed relays to improve the performance of the system. Simulation results show that the proposed pattern achieves significant Inter-Cell Interference (ICI) reduction when compared to other cooperative and noncooperative schemes.

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 score0.988
Threshold uncertainty score0.749

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 science0.0020.001
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 head0.105
GPT teacher head0.329
Teacher spread0.225 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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