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Record W2891216649 · doi:10.1109/tvt.2018.2869946

Achievable Rate Region of the Buffer-Aided Two-Way Energy Harvesting Relay Network

2018· article· en· W2891216649 on OpenAlexaff
Xiaolong Lan, Qingchun Chen, Xiaohu Tang, Lin Cai

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsRelayComputer networkComputer scienceScheduling (production processes)WirelessWireless networkEnergy harvestingEnergy (signal processing)Power (physics)Mathematical optimizationMathematicsTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we investigate the buffer-aided two-way wireless energy harvesting (EH) relay network comprising two users and one relay, where two users exchange information with the help of the relay via three phases of EH, multiple-access, and broadcast. By transforming the opportunistic scheduling design problem into an equivalent convex problem, we present the adaptive design for the buffer-aided two-way EH relay network to maximize the long-term achievable rate region. To fulfill the delay sensitive transmission requirements, a delay-aware adaptive transmission (DAAT) scheduling scheme is proposed to guarantee the average end-to-end delivery delay by employing the Lyapunov optimization framework. Our analysis discloses that the average achievable rate region of the two-way wireless EH relay network can be improved when fully considering the potentials by deploying data buffer and energy storage at the relay. There exists an inherent tradeoff among the achievable sum rate, the delivery delay, and the power consumption. It is shown that, when a certain time delay is tolerable, the DAAT scheduling scheme is able to realize a rate region arbitrarily close to the achievable rate region of the buffer-aided two-way wireless EH relay network.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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
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

Citations30
Published2018
Admission routes1
Has abstractyes

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