AI 中文总结
该研究针对云-边-端协同架构，提出集成感知、通信与计算框架，通过联合优化混合波束成形与计算资源分配，实现感知精度与计算延迟的更优权衡。
AI 中文摘要
本文提出了一种基于云-边-端协同架构的新型集成感知、通信与计算（ISCC）框架，该框架通过复用上行卸载信号实现被动感知，可直接在边缘提取感知信息，且不会产生额外传输开销。然而，这种信号复用会在通信效率与感知覆盖之间引入固有权衡。为应对这一挑战，我们在实际硬件约束下采用混合波束成形架构。此外，感知任务的集成会在移动边缘计算（MEC）服务器处引发显著的资源竞争，其中对延迟敏感的设备任务与计算密集型感知推理任务会争夺有限的处理能力。为减轻该计算负担，我们引入了一种拆分推理机制，可在边缘与云之间战略性地划分智能感知任务。基于此框架，我们构建了一个联合优化问题，以在严格的感知性能约束下最小化所有设备任务的平均计算延迟。为解决所构建问题的高非凸性，我们开发了一种高效的交替优化算法。具体而言，我们设计了一个两层框架，以联合确定最优DNN拆分点与计算资源分配，并采用基于加权最小均方误差（WMMSE）的方法结合流形优化来进行混合波束成形设计。数值结果表明，与基准方案相比，所提出的框架在感知精度与计算延迟之间实现了更优的权衡。
英文摘要
This paper proposes a novel integrated sensing, communication, and computing (ISCC) framework over a cloud-edge-device collaborative architecture, where passive sensing is enabled by reusing uplink offloading signals to extract sensing information directly at the edge without incurring additional transmission overhead. Nevertheless, such signal reuse introduces an inherent tradeoff between communication efficiency and sensing coverage. To address this challenge, we adopt a hybrid beamforming architecture under practical hardware constraints. In addition, the integration of sensing tasks creates significant resource contention at the mobile edge computing (MEC) server, where latency-sensitive device tasks and computation-intensive sensing inference tasks compete for limited processing capacity. To alleviate this computation burden, we introduce a split inference mechanism that strategically partitions intelligent sensing tasks between the edge and the cloud. Building upon this framework, we formulate a joint optimization problem to minimize the average computation latency of all device tasks subject to strict sensing performance constraints. To tackle the high non-convexity of the formulated problem, we develop an efficient alternating optimization algorithm. In particular, we design a two-layer framework to jointly determine the optimal DNN splitting point and computation resource allocation and employ a weighted minimum mean square error (WMMSE)-based approach with manifold optimization for hybrid beamforming design. Numerical results demonstrate that the proposed framework achieves a superior tradeoff between sensing accuracy and computation latency compared to the benchmark schemes.
