This paper initiates the first exploratory study to investigate the robust integrated sensing and communication (ISAC) systems under channel estimation errors from the perspective of GPU-Accelerated bilevel optimization. Within this framework, the upper-level problem is dedicated to simultaneously optimizing communication and sensing objectives, quantified respectively by weighted sum rate and Cram\'er-Rao lower bound, while the lower-level problem considers the channel uncertainties. We then propose an efficient algorithm that can find a set of Pareto optimal solutions with different trade-offs among communication rates and sensing accuracy. The theoretical analysis regarding the convergence rate has also been provided. Furthermore, we design a bilevel optimization inspired deep neural network architecture for that can be realized efficiently on GPU platform. Experiments have been conducted to evaluate the performances of proposed methods. In particular, the proposed GPU-accelerated parallel bilevel optimization can accelerate the convergence speed by up to 50 times compared to conventional gradient-based methods. This characteristic renders it especially suitable for real-time applications, exemplified by the demanding requirements of robust ISAC in upcoming 6G networks.

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming technique that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties.