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Transceiver Design for MIMO-DFRC Systems

2023· article· en· W4372266916 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
TopicRadar Systems and Signal Processing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMIMOComputer scienceCommunications systemWaveformRadarTransceiverElectronic engineeringSignal-to-interference-plus-noise ratioSignal-to-noise ratio (imaging)Interference (communication)Antenna (radio)Power (physics)BeamformingEngineeringTelecommunicationsWireless

Abstract

fetched live from OpenAlex

This paper addresses joint design of the transmitting waveform and the receivers of a dual-function radar-communication (DFRC) system that enables both multiple-input multiple-output (MIMO) radar sensing and multi-user multiple-input single-output (MU-MISO) communications. The proposed approach incorporates the design of the communication receiving (Rx) coefficients, in addition to the radar Rx filters. We seek to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR) over multiple targets, subject to per-antenna power constraints, peak-to-average-power ratio (PAPR) constraints and a communication SINR constraint for each user. A successive convex approximation algorithm is developed to find a good solution for the resultant nonconvex design problem. Numerical results show that by incorporating the communication Rx coefficients into the joint design, the radar and communication capabilities of the DFRC system can be significantly enhanced over the state-of-the-art designs.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.291

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 head0.042
GPT teacher head0.229
Teacher spread0.188 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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