Integrated Sensing, Computation, and Communication: System Framework and Performance Optimization
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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. It can reduce the overhead of computation resource by detecting whether the sensing target is static. Subsequently, we analyze the sensing performance of the proposed framework and theoretically prove its effectiveness with the help of the sampling theorem. Then, we formulate a sensing accuracy 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 minimal resource is allocated to computation tasks, and the rest is devoted to sensing tasks. Besides, a threshold selection policy is derived. Compared with the conventional schemes, 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, and extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.
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