Edge Intelligence for 5G and Beyond Networks

- Overview

Edge intelligence for beyond 5G (B5G) networks, also known as 6G, uses artificial intelligence (AI) systems at the edge of the network to manage resources and improve performance. This can help meet the demands of new applications that require low latency and high reliability, such as self-driving cars, smart energy, and virtual reality. 

Here are some ways edge intelligence can improve B5G networks:

• Resource management: AI systems can help schedule resources efficiently in complex environments with many devices and heterogeneous resources. This can also provide better insight into the operating environment.
• User-centricity: Networks can use distributed federated learning algorithms to assess user demands and include user experience in the feedback loop. For example, edge nodes can perform user sentiment analysis and inform the network of any changes that are needed.
• Data acquisition: Edge intelligence can enable local data acquisition at the edge of the network.
• Computational processing: Edge intelligence can provide powerful computational processing.

- Edge Intelligence

Edge intelligence, also known as Edge AI, is the combination of machine learning (ML) and edge computing to process data closer to its source. This technology allows devices to act as mobile edge servers, with additional processing power to reduce latency and offload tasks. 

Edge intelligence can be used in a variety of applications, including: smart manufacturing, video analysis for security and safety, automotive, intelligent city furniture, and virtual reality. 

Some benefits of edge intelligence include: Real-time operational awareness, Reduced electricity costs, Increased renewable energy accommodation, and Reduced carbon emissions. 

Here are some examples of edge intelligence products:

• Advantech's Edge Intelligence Server: A solution for managing data from semiconductor equipment 
• Ivanti's Edge Intelligence: A product that provides real-time operational IT awareness 
• Cognex's Edge Intelligence: A product that collects and analyzes performance trends, monitors configuration changes, and captures image

- 5G Edge Intelligence

 5G Edge Intelligence is a new concept that uses mobile edge computing and edge caching capabilities to provide AI to end users. 

Edge AI provides real-time intelligence, which enables immediate decision-making. It also allows edge devices to autonomously process and analyze data. 

Edge computing, when combined with 5G, can: 

• Enhance digital experiences
• Improve performance
• Support data security
• Enable continuous operations in every industry
• Render 3D images for AR/VR applications
• Ensure that applications are comfortable for the end-user

5G Edge is important for the deployment of large-scale IoT applications and services. It also supports emerging use cases that are grounded in next-generation technologies like AI, augmented reality (AR), and virtual reality (VR). 

- Edge Intelligence for Beyond 5G Networks

Beyond fifth-generation (B5G) networks, or so-called “6G”, is the next-generation wireless communications systems that will radically change how Society evolves. 

Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with AI to the proximity of end users.

In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. 

AI helps to obtain efficient resource scheduling strategies in a complex environment with heterogeneous resources and a massive number of devices, while meeting the ultra-low latency and ultra-high reliability requirements of novel applications, e.g., self-driving cars, remote operation, intelligent transport systems, Industry 4.0, smart energy, e-health, and AR/VR services.

By integrating AI functions into edge networks, radio networks become service-aware and resource-aware to have a full insight into the operating environment and can adapt resource allocation/orchestration in a dynamic manner. 

Despite the potential of edge intelligence, however, many challenges also need to be addressed in this new paradigm. Until now, limited research efforts have been made on edge intelligence for B5G networks.