Edge AI for Ultra–Reliable Low Latency Communication (URLLC) In 6G Networks

Abstract

Wireless technology development prepares the field for sixth-generation (6G) networks targeting some mission-critical scenarios with Ultra-Reliable Low Latency Communication (URLLC) needs. Examples of these scenarios are: autonomous vehicles, remotely controlled industrial machinery, distance surgery, and augmented reality. All of these require a nearinstant response. Traditional cloud-based models for processing are grossly inadequate with these demands due to the latency associated with data transfer and centralized computation/processing. In this regard, Edge Artificial Intelligence (AI) (Edge AI) is emerging as a game-changer for these applications by allowing real-time data processing and decision making at the edge of the network (i.e., nearer to the devices). This paper examines the application of Edge AI on 6G architectures toward supporting URLLC with a focus on intelligent resource allocation, network slicing, and adaptive control. It also looks at some main issues such as computation offloading, model solution processes, and energy-efficient computation. It is shown that with Edge AI, the requirements of latency and reliability associated with URLLC are adequately fulfilled, along with added scalability and resilience in future-generation communication networks. The next-generation communication systems will withstand additional operational loads without affecting responsiveness and reliability. This study looks into designing and building advanced 6G systems for real-time and responsive intelligent systems across different sectors.