We have seen in earlier blog posts that edge computing enables new business opportunities across multiple industries. To understand what or how 5G enhances these use cases, let’s first get familiar with some 5G-related terminology:

• gNB: gNodeB is the 5G base station that transmits and receives communications between the user equipment (UE) and the mobile network. It uses new radio access technology called New Radio (NR).
• mmWave: Millimeter waves are typically used in 5G. They have a shorter range than microwaves, therefore the cells are limited to smaller sizes; the waves also have trouble passing through building walls. Millimeter wave antennas can be (and thus are) smaller than the large antennas used in previous cellular networks.
• MEC: Multi-access Edge Computing is a network architecture where more processing, especially for latency-sensitive applications, stays closer to the edge of the mobile network.
• NFV: Network Function Virtualization is a network architecture concept that uses the technologies of IT virtualization to virtualize entire classes of network node functions.
• RAN: Radio Access Network is what connects user equipment to other parts of a mobile network via a radio connection. 5G allows for virtual Radio Access Network (vRAN) in addition to traditional physical network components.
• SDN: Software Defined Networking is the approach of using open protocols for remote configuration of network switches and routers.
• UPF: User Plane Function is the 5G equivalent of the packet gateway. This function includes features to support packet routing and forwarding, interconnection to other data networks, and policy enforcement. UPF is also referred to as the data plane.

To meet the requirements for scale, throughput, latency, and reliability, 5G architecture has adopted Network Function Virtualization (NFV) and native cloud standards/technologies to streamline network and service deployment, operations, and management.

Leveraging 5G features like enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine Type Communications (mMTC), edge computing use cases broadly fall into five categories, as shown in Figure 1. These categories represent a subset of all Multi-access Edge Computing (MEC) applications.

Let’s elaborate each of these categories:

• Industry 4.0: This category involves gathering deep insights from distributed machinery and manufacturing processes and responding in real time, which, in turn, optimizes production lines and reduces waste.
• Broadband Everywhere: This category involves the delivery of broadband to the wider public with high quality of service (QoS), based on one of the biggest 5G benefits of higher capacity and enhanced connectivity.
• Connected Infrastructure: This category involves creating new connected experiences that pull and analyze data from distributed devices and sensors to improve individual experiences, enhance driver safety, and optimize transport.
• User Experience: This category involves immersive user experiences and potential for peer-to-peer delivery of content, based on decreased latency and improved bandwidth. It is further aided by real-time feedback and user specific content.
• Augmented Reality/Virtual Reality: This category involves AR/VR applications aided by the use of GPU (Graphics Processing Unit) accelerators in devices in industries like gaming, entertainment, healthcare, and retail.

Given that 5G is closely related to telecom, a topology is best illustrated by a telco network topology with 5G and edge computing components, as depicted in Figure 2.

Some of the key components that make up the above topology are as follows:

• Cloud: A public cloud or private cloud that acts as a repository for cloud native, container-based workloads. These clouds also host and run applications that are used to orchestrate and manage the different edge computing nodes.
• Network edge: The network edge supports the deployed 5G network, including portions of the Virtual Radio Access Network (vRAN). The core network functions include 5G core, control plane, user plane, and location.
• Edge cluster/gateway: The edge cluster/gateway supports the running of 5G edge network components and Multi-access Edge Computing (MEC) applications. The application may send/receive traffic to end users/devices directly from the access network. An application may be a provider and/or a consumer of MEC services.
• Edge devices: Edge devices have compute and storage that can be utilized to run container-based applications, including on manufacturing robots in a factory floor, an ATM, an intelligent camera, or an automobile.
• Sensors: Sensors, though not shown, are IoT-type devices with fixed functions.

As shown above in Figure 2, sensors and other far edge devices (as they are sometimes referred to) capture relevant information. Edge devices may also perform processing of data before sending it to the MEC, while sensors will transmit data as-is to the MEC. A 5G network will transmit the data through the radio heads and vRAN to the application running on the MEC. The application may also run on the core network or other backhaul components instead of the MEC, but such applications will have higher latency than those running on the MEC.

Previous blogs have described how a product like IBM Edge Application Manager (IEAM) can autonomously deploy workloads to remote edge nodes and can manage these workloads. This is done from a management hub cluster running Red Hat OpenShift Container Platform or other Kubernetes-based clusters. The IEAM management hub is designed specifically for edge node management to minimize deployment risks and to fully manage the service software lifecycle on edge nodes autonomously.

True 5G applications are latency sensitive and telco’s network edge Public/Private MEC enables new edge nodes for deployment. IEAM’s policy-based configuration function not only simplifies the placement of workloads, but does it autonomously and at scale.

With 5G and smart edge computing devices, we expect to see a surge in data at the edge, and data combined with AI can fuel innovation. The ability to harness the power of data and edge computing by leveraging real-time edge analytics is revolutionizing the digital landscape.

The IBM Cloud architecture center offers up many hybrid and multicloud reference architectures, including AI frameworks. Look for the edge computing reference architecture and related articles.

This blog introduced telecom edge and showed how 5G and edge technologies are inextricably linked. We shared our insights on 5G and provided examples of emerging 5G edge use cases. We hope this article sparks new thinking and innovation around the power of 5G and edge can bring.

Thanks to Jason Gonzalez for reviewing the article. Please make sure to check out all the installments in this series of blog posts on edge computing:

• Part 1: “Cloud at the edge”
• Part 2: “Rounding out the edges”
• Part 3: “Architecting at the edge”
• Part 4: “DevOps at the edge”
• Part 5: “Policies at the edge”
• Part 6: “Models deployed at the edge”
• Part 7: “Security at the edge”
• Part 8: “Analytics at the edge”
• Part 9: “5G at the edge”
• Part 10: “Clusters at the edge”
• Part 11: “Automation at the edge”
• Part 12: “Network slicing at the edge”
• Part 13: “Data at the edge”
• Part 14: “Architectural decisions at the edge”
• Part 15: “GitOps at the edge”
• Part 17: “Storage services at the edge”
• Part 18: “Cloud services at the edge”
• Part 19: “Distributed cloud: Empowerment at the edge”
• Part 20: “Data sovereignty at the edge”
• Part 21: “Solutioning at the edge”
• Part 22: “Connected products at the edge”
• Part 23: “Foundational models at the edge”

• IBM Garage
• IBM Edge Application Manager
• IBM Cloud Pak for Network Automation
• 5G Edge Computing
• 5G Network Slicing Use Cases
• 5G Terminology
• Telecom’s 5G Future
• IBM Cloud for Telecommunications