Integrated Sensing-Communication-Computation for Edge Artificial Intelligence

TL;DR

This paper explores integrated sensing, communication, and computation (ISCC) schemes to optimize resource utilization and performance in edge AI tasks like federated learning and inference for 6G applications.

Contribution

It investigates the interplay among sensing, communication, and computation modules, proposing various ISCC schemes for edge AI in both application and physical layers.

Findings

Enhanced resource utilization through ISCC schemes.

Improved performance of edge AI tasks with integrated modules.

Frameworks for federated learning and inference in edge AI.

Abstract

Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything. The performance of edge AI tasks, including edge learning and edge AI inference, depends on the quality of three highly coupled processes, i.e., sensing for data acquisition, computation for information extraction, and communication for information transmission. However, these three modules need to compete for network resources for enhancing their own quality-of-services. To this end, integrated sensing-communication-computation (ISCC) is of paramount significance for improving resource utilization as well as achieving the customized goals of edge AI tasks. By investigating the interplay among the three modules, this article presents various kinds of ISCC schemes for federated edge learning tasks and edge AI inference tasks in both application and physical layers.