Beamforming Optimization for Robust Sensing and Communication in Dynamic mmWave MIMO Networks

Acquiring accurate channel state information (CSI) at low overhead is crucial for millimeter wave MIMO communications but is challenging in dynamic environments. In this work, we exploit the emerging integrated sensing and communication (ISAC) beamforming technique for concurrent CSI sensing and data transmission. Despite its low overhead, the corresponding ISAC transmit beamforming design faces a complex trade-off between CSI sensing accuracy and communication interference management. To address this, we formulate the beamforming design as an optimization problem minimizing the maximum Cramér-Rao bound (CRB) of CSI sensing errors subject to the users’ worst-case communication rates under CSI errors. To efficiently solve the problem, we step-by-step propose three algorithms. The first algorithm is based on the semidefinite relaxation and successive convex optimization techniques, which can serve as a benchmark algorithm but suffers high computational complexity. To efficiently handle the worst-case objective and rate constraints, we propose a complexity-reduced algorithm based on the primal-dual optimization method and first-order min-max algorithm. Furthermore, we dismiss SDR and employ the block coordinate descent method combined with cheap gradient descent steps to achieve a low-complexity algorithm. Extensive simulations show the proposed ISAC beamforming design and low-complexity algorithms can provide robust communication performance and significantly outperform existing schemes.