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Record W2127603053 · doi:10.1109/cwit.2011.5872158

Green resource allocation with QoS provisioning for cooperative cellular network

2011· article· en· W2127603053 on OpenAlexaff
Umesh Phuyal, Satish C. Jha, Vijay K. Bhargava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceQuality of serviceRelayProvisioningResource allocationTransmitter power outputComputer networkThroughputEnergy consumptionCellular networkBase stationTransmission (telecommunications)Efficient energy usePower (physics)WirelessTelecommunicationsEngineeringChannel (broadcasting)Transmitter

Abstract

fetched live from OpenAlex

Relay-based cooperative transmission in cellular network has been an area of tremendous research recently. Transmission via relays introduces power consumption at both the source and relay stations which may lead to less efficient system in terms of power consumption. Because of increasing energy cost for cellular systems and concern over environmental issues, an energy efficient design of resource allocation scheme in cooperative cellular network is of prime importance. In this paper, we propose a novel resource allocation scheme in order to maximize the energy aware system performance. The proposed low-complexity scheme allocates powers for base station and relay by using a strategy that minimizes required transmit power per unit achieved throughput and at the same time guarantees a predefined quality of service (QoS) which is specified in terms of minimum end-to-end data rate required by each user. Simulation results show that proposed scheme outperforms existing power allocation schemes by decreasing required power to guarantee the QoS without increasing system outage penalty, which is essential for green communication systems.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.053
GPT teacher head0.249
Teacher spread0.196 · 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 designTheoretical or conceptual
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

Citations9
Published2011
Admission routes1
Has abstractyes

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