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arXiv 2211.04022cs.ITmath.IT
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通感算一体化:系统框架与性能优化

Integrated Sensing, Computation, and Communication: System Framework and Performance Optimization

Yinghui He, Guanding Yu, Yunlong Cai, Haiyan Luo

AI总结:

针对ISCC系统中感知与计算任务的资源竞争,提出带动作检测模块的高效感知框架,并给出最优资源分配与阈值选择策略,实验验证其性能提升。

摘要:

通感算一体化(ISCC)近来被认为是面向5G后系统的一项有前景的技术。在ISCC系统中,面向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和计算资源的竞争,正成为一个日益严峻的问题。为解决该问题,我们首先提出一种高效感知框架,其中包含一个新颖的动作检测模块。在该模块中,使用一个阈值来检测感知目标是否静止,从而降低开销。随后,我们对所提框架的感知性能进行数学分析,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,我们构建了一个在保障任务服务质量(QoS)需求的同时最大化感知性能的问题。为求解该问题,我们提出一种最优资源分配策略:将最少资源分配给计算任务,其余资源全部用于感知任务。此外,我们推导了阈值选择策略,结果进一步证明了所提感知框架的必要性。最后,基于USRP B210开展动作识别任务的真实世界测试,以验证感知性能分析。大量实验通过与若干基准方案进行比较,证明了本方案的性能提升。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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