Robust Hybrid Beamforming for Integrated Sensing and Communications via Learned Optimization

Robust hybrid beamforming for integrated sensing and communications (ISAC) system under bounded uncertainties in sensing reception is developed using algorithm unrolling technique. First, the robust hybrid beamforming design problem is formulated as an optimization problem that jointly maximizes the communication sum-rate and the worst-case sensing mutual information under the uncertainty of receive steering vector. Then, a benchmark method using projected gradient descent and ascent (PGDA) algorithm is designed to solve this optimization problem. Finally, we propose to unroll the developed PGDA algorithm using the algorithm unrolling technique. Numerical results demonstrate the advantages of the unrolled PGDA algorithm over the PGDA benchmark for addressing the newly introduced problem of robust hybrid beamforming design for ISAC.