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Record W4287238742 · doi:10.48550/arxiv.2104.02849

Relay-Reconfigurable Intelligent Surface Cooperation for\n Energy-Efficient Multiuser Systems

2021· preprint· en· W4287238742 on OpenAlexaff
Mohanad Obeed, Anas Chaaban

Bibliographic record

VenuearXiv (Cornell University) · 2021
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsRelayTelecommunications linkBeamformingComputer scienceBase stationTransmitterTransmitter power outputQuality of servicePower (physics)Singular value decompositionEfficient energy useElectronic engineeringComputer networkEngineeringTelecommunicationsElectrical engineeringAlgorithmPhysics

Abstract

fetched live from OpenAlex

Reconfigurable intelligent surfaces (RIS) have drawn considerable attention\nrecently due to their controllable scattering elements that are able to direct\nelectromagnetic waves into desirable directions. Although RISs share some\nsimilarities with relays, the two have fundamental differences impacting their\nperformance. To harness the benefits of both relaying and RISs, a multi-user\ncommunication system is proposed in this paper wherein a relay and an RIS\ncooperate to improve performance in terms of energy efficiency. To utilize the\nRIS efficiently, the discrete phase shifts of the RIS elements are optimized\nalong with the beamforming matrices at the transmitter and the relay, targeting\nthe minimization of the total transmit power subject to a quality-of-service\n(QoS) constraint. Then, two suboptimal efficient solutions are proposed for the\nresulting discrete and non-convex problem, one based on singular value\ndecomposition (SVD) and uplink-downlink duality and the other is based on SVD\ncombined with zero-forcing. Simulations show that the proposed solutions\noutperform a system with either a relay or an RIS only, especially when both\nare closer to the users than to the base-station.\n

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.755
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.063
GPT teacher head0.185
Teacher spread0.121 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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