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Record W2915417289 · doi:10.1109/glocom.2018.8647540

Joint Power Control and Routing in Multihop D2D Assisted Cellular Systems

2018· article· en· W2915417289 on OpenAlexaff
Ahmed Ibrahim, Telex M. N. Ngatched, Octavia A. Dobre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTelecommunications linkComputer scienceBase stationPower controlUnderlayUser equipmentSignal-to-noise ratio (imaging)Interference (communication)Mathematical optimizationSignal-to-interference-plus-noise ratioPower (physics)Computer networkMathematicsTelecommunications

Abstract

fetched live from OpenAlex

This paper considers a cellular system with multihop device-to-device (D2D) communications to extend the coverage of a cell, for user equipment (UEs) experiencing service outage. The D2D system we consider is an inband underlay system. The target is to perform power control and routing to minimize the expended UE energy while satisfying the signal-to-noise-ratio (SNR) requirements on the downlink connections on every pre-allocated resource block (RB), signal-to-interference-plus- noise-ratio (SINR) requirements for every D2D sidelink connection on every pre-allocated RB, and a maximum allowable interference at the base- station (BS) receiver on all uplink frame RBs. An optimization problem is formulated that turns out to be a mixed integer non-linear program. We propose the generalized Benders decomposition which breaks down the formulation to a master sub-problem, an auxiliary sub-problem and a feasibility sub-problem. In this paper, we focus on finding efficient solution methods for the auxiliary sub-problem that is necessary for generating under-estimators to one of two types of Benders' cuts. These cut-of infeasible power allocations in a relaxed version of the master problem, and tighten the lower bounds on the optimal objective function value. Simulations are conducted to show how multihop D2D routing improves the cell coverage, by reducing the probability of service outage. The simulation results also show the effect of the requirements of SNR, SINR and interference at the BS on the expended UE energy.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score0.460

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 science0.0000.000
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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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