A zero-feedback FDD MIMO ISAC precoding framework uses observed Fisher information for error covariance estimation and RSMA with NEPv-based power iteration to maximize spectral efficiency under a beam pattern constraint.
Robust Integrated Sensing and Communication Beamforming for Dual-functional Radar and Communications: Method and Insights
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abstract
This work presents a novel robust beamforming design dedicated for dual-functional radar and communication (DFRC) base stations (BSs) in the context of integrated sensing and communications (ISAC). The architecture is intended for circumstances with imperfect channel state information (CSI). Our suggested approach demonstrates several tradeoffs for joint radar-communication deployment. Due to the DFRC nature of the design, the beamformer can simultaneously point towards an intended target, while optimizing communication quality of service. We unveil several insights regarding closed form expressions, as well as optimality of the proposed beamformer. Lastly, simulation results demonstrate the effectiveness of the proposed ISAC beamformer.
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Integrated Sensing and Communications in Downlink FDD MIMO without CSI Feedback
A zero-feedback FDD MIMO ISAC precoding framework uses observed Fisher information for error covariance estimation and RSMA with NEPv-based power iteration to maximize spectral efficiency under a beam pattern constraint.