Integrated Sensing-Communication-Computation for Edge Artificial Intelligence
This addresses resource optimization for edge AI applications in 6G technologies, such as digital twins and auto-driving, but is incremental as it builds on existing integration concepts.
The paper tackles the resource competition among sensing, communication, and computation modules in edge AI tasks by proposing integrated sensing-communication-computation (ISCC) schemes to improve resource utilization and achieve customized goals for tasks like federated edge learning and edge AI inference.
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.