Sensing Framework Design and Performance Optimization with Action Detection for ISCC

TL;DR

This paper introduces a resource-efficient sensing framework with action detection for ISCC systems, optimizing sensing accuracy under resource constraints and validated through real-world tests.

Contribution

It proposes a novel distributed sensing framework with action detection and an ADMM-based optimization algorithm for resource allocation in multi-device ISCC systems.

Findings

Achieves higher sensing accuracy compared to baselines.

Reduces sensing overhead by selective offloading.

Validated with real-world wireless device tests.

Abstract

Integrated sensing, communication, and computation (ISCC) has been regarded as a prospective technology for the next-generation wireless network, supporting humancentric intelligent applications. However, the delay sensitivity of these computation-intensive applications, especially in a multidevice ISCC system with limited resources, highlights the urgent need for efficient sensing task execution frameworks. To address this, we propose a resource-efficient sensing framework in this paper. Different from existing solutions, it features a novel action detection module deployed at each device to detect the onset of an action. Only time windows filled with signals of interest are offloaded to the edge server and processed by the edge recognition module, thus reducing overhead. Furthermore, we quantitatively analyze the sensing performance of the proposed sensing framework and formulate a sensing accuracy maximization problem under power, delay, and resource limitations for the multi-device ISCC system. By decomposing it into two subproblems, we develop an alternating direction method of multipliers (ADMM)-based distributed algorithm. It alternatively solves a sensing accuracy maximization subproblem at each device and employs a closed-form computation resource allocation strategy at the edge server till convergence. Finally, a real-world test is conducted using commodity wireless devices to validate the sensing performance analysis. Extensive test results demonstrate that our proposal achieves higher sensing accuracy under the limited resource compared to two baselines.