GPU-Accelerated Parallel Bilevel Optimization for Roubst 6G ISAC

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 Cramér-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.