MétaCan
Menu
Back to cohort

Performance of MIMO-NOMA-ISAC Based on Signal Alignment

2024· article· en· W4400276064 on OpenAlexaff
Boqun Zhao, Chongjun Ouyang, Xingqi Zhang, Yuanwei Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNomaComputer scienceMIMOSIGNAL (programming language)Electronic engineeringTelecommunicationsEngineeringBeamformingTelecommunications linkProgramming language

Abstract

fetched live from OpenAlex

This paper proposes a framework of multiple-input multiple-output based non-orthogonal multiple access for Integrated sensing and communications (ISAC), which incorporates signal alignment to enhance system performance. Sensing rate (SR) and communication rate (CR) are derived under three different precoding designs: sensing-centric design, communications-centric design, and Pareto optimal design. Moreover, the SR-CR regions achieved by ISAC and frequency-division sensing and communications (FDSAC) are studied. Numerical results reveal that ISAC outperforms FDSAC in terms of both SR and CR and can achieve a broader rate region, clearly showcasing its superiority over the conventional FDSAC.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.271

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Wireless Communication TechnologiesFrench-language works237,207