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Record W2088397325 · doi:10.1109/jsac.2012.120214

Joint Zero-Forcing Based Precoder Design for QoS-Aware Power Allocation in MIMO Cooperative Cellular Network

2012· article· en· W2088397325 on OpenAlexaff
Umesh Phuyal, Satish C. Jha, Vijay K. Bhargava

Bibliographic record

VenueIEEE Journal on Selected Areas in Communications · 2012
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceBase stationTransmitter power outputMIMOComputer networkQuality of serviceRelayRSSEnhanced Data Rates for GSM EvolutionCellular networkTransmission (telecommunications)Interference (communication)Real-time computingPower (physics)Channel (broadcasting)TelecommunicationsTransmitter

Abstract

fetched live from OpenAlex

We study a cellular system scenario where multiple data streams originating from a base station (BS) targeted to multiple cell-edge mobile stations (MSs) are transmitted via pre-installed cooperative relay stations (RSs) with multiple antennas. Our objective is to guarantee quality-of-service (QoS) in terms of predefined signal-to-noise ratio at such users within the transmit power budgets at BS and RSs while minimizing total transmit power. We propose a novel precoder design method for power allocation between multiple data streams at BS and RS by using joint zero-forcing strategy in order to avoid multiuser interference (MUI) in the signal received by MSs via both the direct and relay links. We also propose low-complexity suboptimal power allocation algorithm. We focus on analyzing the significance of direct link transmission in providing QoS to cell-edge MSs specially when RS is not situated directly between BS and MSs. Simulation results show that considering direct link and using the proposed scheme in such case significantly improves system outage performance compared to existing schemes in the literature which do not consider direct link.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.099
GPT teacher head0.311
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.

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
Published2012
Admission routes1
Has abstractyes

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