Optimizing Network Performance With Edge Computing for 5G Networks

The rollout of 5G mobile networks has changed the way we connect, giving us access to faster speeds, lower latency and the ability to support a huge number of devices. However, realizing all the benefits of 5G requires optimizing network performance — and edge computing for 5G networks plays a crucial role in achieving this goal. 

Below, we’ll explore how edge computing enhances network performance, why it matters and real-world applications of the technology in 5G.

Why Network Performance Matters in 5G

How well a 5G network performs heavily depends on how efficiently it can handle the high-speed data demands of modern applications. From autonomous vehicles to smart cities, 5G networks must provide users with real-time communication and rapid data exchange — something traditional network infrastructures struggle to meet (at least not without creating bottlenecks). 

Because traditional network architectures rely on processing data at centralized locations, it creates a significant delay in data processing as data has to travel long distances. This model just isn’t efficient for mobile edge computing use cases such as cloud gaming or the response times needed for remote surgery or automated reality (AR).

This is where edge computing telecom strategies come into play. Edge computing refers to availability of computing power and the decentralization of data processing, moving it from central servers to locations closer to the network’s edge (such as base stations or local data centers). In doing so, the network performance for 5G and the processing times for innovative edge applications are significantly improved.

How Edge Computing Optimizes Network Performance for 5G

Now that you understand how edge computing works in 5G networks, let’s look at three ways the technology can optimize network performance.

1. Improving Speed and Throughput

With data processed locally at the network’s edge, edge computing reduces the travel time of data, improving the speed and throughput of 5G networks. Whether it’s video streaming or complex machine-to-machine communication in industrial settings, edge computing for 5G networks allows for a faster, more seamless user experience by reducing data congestion.

2. Reducing Latency

Latency, or the delay in data transmission, is one of the key challenges in achieving the full potential of 5G. Edge computing dramatically reduces latency by keeping data processing as close to the user as possible. This is especially important for real-time applications like autonomous vehicles, AR/VR and remote surgery — all of which rely on near-instantaneous data exchange.

3. Supporting Bandwidth-Intensive Applications

Edge computing also plays a key role in supporting bandwidth-intensive applications like IoT and smart city deployments. By offloading data processing to the network’s edge, edge computing helps reduce the strain on the core network, allowing for more efficient use of bandwidth.

The Role of Multi-Access Edge Computing (MEC) in Telecom

Another key component of edge computing in 5G networks is multi-access edge computing (MEC). The approach moves data processing and storage to the edge of telecom networks — closer to end-users. While traditional edge computing focuses on decentralizing data from cloud centers, MEC integrates directly into telecom infrastructure, optimizing communication for mobile users. 

For telecom operators, offloading processing tasks to the network’s edge, MEC reduces strain on central data centers, lowering operational costs while improving network efficiency. The result is enhanced user experiences and optimized resource utilization. Ultimately, MEC makes it easier for service providers to support bandwidth-heavy, low-latency applications like IoT and augmented reality.

For example, Verizon uses edge computing within its own network to provide its customers with ultra-low latency for applications like AR, VR and IoT. This also enables the company to provide customers with a superior experience with lower lag and higher performance across its 5G network.

Top Challenges for Bringing Edge Computing to 5G Networks

Though edge computing does play a key role in enhancing network performance and increasing user satisfaction, it also introduces a few technical and operational challenges, including:

• Integrating edge nodes with existing infrastructure
• The complexity of managing distributed data
• Ensuring robust security measures at the network’s edge
• The need for skilled professionals to manage multi-access edge computing infrastructure
• Ensure compliance with security standards and protocols

To overcome these challenges, telecom operators should focus on developing standardized frameworks for edge computing telecom deployments (like Project Sylva, for example) and collaborate with industry partners. Investing in talent and robust security protocols is essential for ensuring a smooth implementation of edge computing within 5G networks. Additionally, creating a more adaptable edge computing infrastructure platform allows for a smoother deployment and management of containerized applications, allowing for increased performance, reliability and scalability.

Harnessing Edge Computing for 5G Network Optimization

As 5G networks continue to expand, edge computing is set to be a key part in optimizing network performance. By improving speed, reducing latency and supporting bandwidth-heavy applications, edge computing for 5G networks ensures telecom operators can meet the growing demands of consumers and businesses. 

Unlock the benefits of edge computing in your 5G networks and stay ahead in a rapidly evolving digital landscape. Learn more in our white paper, “Vision for Telecom at the Edge”.