MiLAC-aided MIMO radar achieves identical CRB and DoA performance to fully-digital baselines while cutting hardware via analog-domain beamforming and 2D-DFT.
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Derives closed-form DG-optimal and MSE-optimal transceiver designs for ISAC under compress-and-estimate framework, with numerical results showing DG-optimal design is more power-efficient at low SNR by selective feature allocation.
A Bayesian distributed ISEA system uses a Gaussian-mixture prior for an RWB estimator at sensing and derives threshold-based optimal power allocation at communication to gain inference performance.
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Microwave Linear Analog Computer (MiLAC)-Aided MIMO Radar Sensing: Transmit Beamforming Design and DoA Estimation
MiLAC-aided MIMO radar achieves identical CRB and DoA performance to fully-digital baselines while cutting hardware via analog-domain beamforming and 2D-DFT.
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Inference-Optimal ISAC via Task-Oriented Feature Transmission and Power Allocation
Derives closed-form DG-optimal and MSE-optimal transceiver designs for ISAC under compress-and-estimate framework, with numerical results showing DG-optimal design is more power-efficient at low SNR by selective feature allocation.
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Distributed Integrated Sensing and Edge AI Exploiting Prior Information
A Bayesian distributed ISEA system uses a Gaussian-mixture prior for an RWB estimator at sensing and derives threshold-based optimal power allocation at communication to gain inference performance.