Integrated sensing, computation, and communication (ISCC) has been recently considered as a promising technique for beyond 5G systems. In ISCC systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module. In this module, a threshold is used for detecting whether the sensing target is static and thus the overhead can be reduced. Subsequently, we mathematically analyze the sensing performance of the proposed framework and theoretically prove its effectiveness with the help of the sampling theorem. Based on sensing performance models, we formulate a sensing performance maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy, in which the minimum resource is allocated to computation tasks, and the rest is devoted to the sensing task. Besides, a threshold selection policy is derived and the results further demonstrate the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis. Extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.

翻译：集成感知、计算与通信（ISCC）近年来被视为超越5G系统的一项有前景的技术。在ISCC系统中，用于环境智能的感知任务与来自移动设备的计算任务之间对通信与计算资源的竞争日益成为一项挑战性问题。为解决这一问题，我们首先提出了一种高效感知框架，其中包含一个新颖的动作检测模块。该模块利用阈值判断感知目标是否处于静止状态，从而降低开销。随后，我们从数学角度分析了所提框架的感知性能，并借助采样定理从理论上证明其有效性。基于感知性能模型，我们在保证任务服务质量（QoS）要求的前提下，构建了感知性能最大化问题。为求解该问题，我们提出了一种最优资源分配策略，将最少的资源分配给计算任务，剩余资源则用于感知任务。此外，推导了阈值选择策略，结果进一步证实了所提感知框架的必要性。最后，基于USRP B210平台开展了真实环境下的动作识别任务测试，以验证感知性能分析结果。通过与若干基准方案进行对比，大量实验证明了我们方法的性能提升。