上海科技大学知识管理系统

This paper investigates an integrated sensing-communication-computation (ISCC) based multi-view-multi-task (MVMT) edge AI inference system. Each device senses a narrow view of a target area and processes the echo signal to generate real-time sensory data. An edge server receives and combines multiple views of data from multiple devices to complete several downstream inference tasks. Compared with existing designs where dedicated sensory data is obtained, transmitted, and processed for each task, this ISCC-based MVMT framework enjoys reduced costs of sensing, on-device computation, and communication overhead due to data sharing among different tasks. The challenges of improving all tasks’ inference accuracy lie in the tight coupling of sensing, communication, and computation among different devices and sensory view competition among different tasks. These two challenges intertwine, making the multi-task optimization problem mixed-integer non-convex programming. To tackle this problem, we propose a joint device scheduling and resource allocation (JDSRA) scheme, which alternatively solves a subproblem of joint device scheduling and time allocation and a subproblem of resource allocation till convergence. Particularly, in addition to a dynamic-programming-based optimal device scheduling algorithm, a low-complexity suboptimal algorithm is proposed based on sorting a derived closed-form indicator, which represents the increase of all tasks’ inference accuracy per time unit consumption. Besides, a low-complexity optimal resource allocation algorithm is proposed by parallelly solving multiple simple convex subproblems. Numerical results based on jointly completing three tasks of human motion recognition, human height recognition, and localization in smart home scenarios are conducted to verify the performance of our proposed schemes.