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Record W2525987021 · doi:10.1109/tcomm.2016.2613520

Optimal Energy Management in Hybrid Energy Small Cell Access Points

2016· article· en· W2525987021 on OpenAlexaff
Animesh Yadav, Tri Minh Nguyen, Wessam Ajib

Bibliographic record

VenueIEEE Transactions on Communications · 2016
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversité du Québec à MontréalÉcole de Technologie SupérieureMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceTelecommunications linkDecoding methodsNetwork packetMathematical optimizationOnline algorithmReal-time computingAlgorithmComputer networkMathematics

Abstract

fetched live from OpenAlex

In this paper, we consider a multiantenna small-cell access-point serving multiple users on the downlink and uplink sides using the frequency division duplex scheme. The access point is powered by both renewable and non-renewable energy sources. The objective of this paper is to process the frequency division duplex frame by drawing the minimum amount of energy from the non-renewable energy source while guaranteeing the quality of service of downlink transmission and decoding all the received uplink packets. Assuming that the energy used for sampling and decoding the received packets is not negligible, the optimal transmit power allocation and received packet decoding policy is investigated first in an offline setting and then in an online setting. An iterative offline algorithm based on dual decomposition method is proposed to find the optimal policy. In the online setting, an optimal high-complexity solution using dynamic programming approach is developed. Then, a reduced complexity suboptimal online algorithm using dual decomposition is proposed. Finally, two more suboptimal and low-complexity online algorithms for maximizing the ratio of throughput and non-renewable energy, and fairness metric are further addressed. Numerical simulations evaluate the performance of the proposed offline and online algorithms and show the efficiency of our proposed algorithms.

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: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.910

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 head0.024
GPT teacher head0.235
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.

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

Citations12
Published2016
Admission routes1
Has abstractyes

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