Mobile edge computing (MEC) is powerful to alleviate the heavy computing tasks in integrated sensing and communication (ISAC) systems. In this paper, we investigate joint beamforming and offloading design in a three-tier integrated sensing, communication and computation (ISCC) framework comprising one cloud server, multiple mobile edge servers, and multiple terminals. While executing sensing tasks, the user terminals can optionally offload sensing data to either MEC server or cloud servers. To minimize the execution latency, we jointly optimize the transmit beamforming matrices and offloading decision variables under the constraint of sensing performance. An alternating optimization algorithm based on multidimensional fractional programming is proposed to tackle the non-convex problem. Simulation results demonstrates the superiority of the proposed mechanism in terms of convergence and task execution latency reduction, compared with the state-of-the-art two-tier ISCC framework.

翻译：移动边缘计算（MEC）能够有效缓解集成感知与通信（ISAC）系统中的繁重计算任务。本文研究了一种包含云服务器、多台移动边缘服务器及多个终端的三层集成感知、通信与计算（ISCC）框架中的联合波束成形与卸载设计问题。在执行感知任务时，用户终端可选择将感知数据卸载至MEC服务器或云服务器。为最小化执行时延，我们在感知性能约束下联合优化了发射波束成形矩阵与卸载决策变量。针对该非凸问题，提出了一种基于多维分数规划的交替优化算法。仿真结果表明，与现有最先进的两层ISCC框架相比，所提机制在收敛性及任务执行时延降低方面均具有优越性。