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Record W1973118205 · doi:10.1109/glocom.2014.7037461

Achievable rates for uplink communications in LTE-advanced networks with decode-and-forward relays

2014· article· en· W1973118205 on OpenAlexaff
Xiaoxia Zhang, Xuemin Shen, Liang‐Liang Xie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTelecommunications linkComputer scienceOrthogonal frequency-division multiple accessFrequency-division multiple accessEqualization (audio)Transmission (telecommunications)RelayOrthogonal frequency-division multiplexingBit error rateMinimum mean square errorElectronic engineeringComputer networkTelecommunicationsChannel (broadcasting)MathematicsEngineeringEstimatorPhysics

Abstract

fetched live from OpenAlex

This paper studies the cooperative transmission scheme and the achievable rate for uplink communications in an LTE-Advanced cellular network with Type II in-band decode-and-forward relay stations. The physical layer uplink transmission is based on single carrier frequency division multiple access (SC-FDMA) with frequency-domain equalization (FDE). Unlike orthogonal frequency division multiple access (OFDMA) where the users achievable rate is the summation of the rates on all allocated subcarriers, achievable rate of SC-FDMA system has a more complex expression because the subcarriers are transmitted sequentially rather than in parallel. With the joint superposition coding for cooperative relaying, we derive the expressions of the achievable rate for both zero-forcing (ZF) equalization and minimum mean square error (MMSE) equalization. Numerical results are given to verify the derived uplink achievable rates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.314
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2014
Admission routes1
Has abstractyes

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