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RIS-Assisted Joint Sensing and Communications via Fractionally Constrained Fractional Programming

2024· article· en· W4408324952 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsJoint (building)Computer scienceEngineeringStructural engineering

Abstract

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This paper studies an uplink dual-functional sensing and communication system assisted by an active or passive reconfigurable intelligent surface (RIS), whose reflection pattern is optimally configured to trade off sensing and communication functionalities. Specifically, the Bayesian Cramér-Rao lower bound (BCRLB) for sensing is minimized under the quality-of-service (QoS) communication constraints. We show that this problem can be formulated as a fractionally constrained fractional programming (FCFP) problem for which a quadratic transform, originally proposed for the sum-of-ratio fractional programs, can be used to decouple the numerators and denominators in both the objective function and the constraints. In this way, the FCFP is turned into a sequence of sub-problems that are convex except for the constant-modulus amplitude constraints which can be dealt with using a penalty-based method. Numerical results unveil nontrivial beamforming reflection patterns that the RIS can be configured to generate in order to facilitate both sensing and communications. The results demonstrate the effectiveness of the proposed algorithm.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.349

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 head0.024
GPT teacher head0.242
Teacher spread0.218 · 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

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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