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Record W4399526704 · doi:10.1109/lwc.2024.3412974

Mutual Information-Based Integrated Sensing and Communications: A WMMSE Framework

2024· article· en· W4399526704 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

VenueIEEE Wireless Communications Letters · 2024
Typearticle
Languageen
FieldComputer Science
TopicDistributed Sensor Networks and Detection Algorithms
Canadian institutionsConcordia University
FundersNatural Science Foundation of Sichuan ProvinceChina Mobile Research InstituteNatural Science Foundation of Shenzhen City
KeywordsComputer scienceMutual informationComputer networkTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

In this letter, a weighted minimum mean square error (WMMSE) empowered integrated sensing and communication (ISAC) method is investigated. One transmitting base station and one receiving wireless access point are considered to serve multiple users and a sensing target. Inspired by mutual information (MI), a unified framework to link sensing and communication is constructed, and communication MI and sensing MI rates are utilized as the performance metrics under the presence of clutters. In particular, we propose a novel MI-based WMMSE-ISAC method to maximize the weighted sensing and communication sum rate of this system. Such a maximization process is achieved by utilizing the classical method—WMMSE, aiming to better manage the effect of sensing clutters and the interference among users. Numerical results show the effectiveness of our proposed method, and the performance trade-off between sensing and communication is also validated.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.907
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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.018
GPT teacher head0.259
Teacher spread0.241 · 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