A Robust Beamforming for Integrated Sensing and Communications in Edge IoT Devices

We propose a robust beamforming design methodology for integrated sensing and communications (ISACs) beamform, where the beamforming design is investigated under the sensing optimal beamforming designed to overcome the channel uncertainty that arises from the communication system. Under the assumption that the channel state information (CSI) error is elliptically bounded, we study the robust ISAC beamforming design problem with the minimization of the Cramér-Rao bound (CRB) under the signal-to-noise ratio (SINR) threshold constraint. We consider the long-range and near-range cases separately and categorize them into point-target and extended-target for processing. In the point target scenario, we address the problem through distributed optimization using the S-procedure and solve it with the semidefinite relaxation (SDR) method. Meanwhile, in the extended target scenario, we transform the infinite constraints of the robust ISAC design problem into a finite set, employing linear matrix inequalities (LMIs) for equivalent representation. Under specific conditions, we illustrate that the SDR problem in this scenario can yield a rank-1 solution. Simulation results verify the effectiveness of the proposed CRB optimizationmin method and prove its application value in the next generation of Internet of Things devices.