In the transition to 5G, communication service providers (CSPs) have invested billions of dollars in building their infrastructure and virtualizing key components. Funding from the recent Bipartisan Infrastructure Law in the United States will further infuse capital nationwide to expand mobile broadband services, particularly in rural areas.
However, the skilled workforce needed to lay the fiber to support wireless connections, install radios, deploy the core network components, and other essential equipment remains scarce. To manage 5G complexities, like new core functions, cloud implementations, and network monitoring, CSPs require skilled maintenance and operations crews that aren’t available. In the past four years, telecommunications employees have decreased by nearly 100,000.
As a result of this shortage, network operators must recognize the promise of automation tools to identify and resolve issues, prioritizing their employees’ attention toward more significant problems that could significantly impact business.
The question is to what degree can network operators rely on automation to streamline network management, proactively resolve issues, and optimize resource allocation?
Using automation tools to manage 5G complexities, prioritize employee focus, and reduce downtime
As CSPs migrate to 5G, operationalizing and maintaining a 5G network is no small feat. Companies struggle with legacy processes and often lack knowledge of the technologies needed to integrate 5G capabilities efficiently atop their existing framework.
Unfortunately, the need to manage new, complex tasks is happening simultaneously with the 5G labor shortage, subjecting network operators to a lack of support in making informed decisions and taking decisive actions.
Supporting a 5G standalone (SA) network is one of the most complex transitions carriers undertake. This migration is a giant leap compared to going from 3G to 4G. Beyond the radio access nodes that started to change with 5G non-standalone (NSA), 5G SA comes with a new set of core network functions. With those core network functions, we also see the introduction of new message formats and procedures, and the option of encryption on the service-based interfaces (SBIs), creating further challenges.
Additionally, as the transition from dedicated appliances to highly complex virtualized cloud-native architectures speeds up, accessing 5G packets between new network functions becomes far more difficult. For some carriers, the 4G and 5G nodes are collapsing, offering a single, dual-functional node. What this means for service assurance is that CSPs will be compelled to monitor their 4G network simultaneously with the 5G SA network to perform any triage as the migration to Voice over New Radio (VoNR) takes place. With 6G on the horizon, these challenges will only add complexities, so CSPs must consider employing sophisticated automated tools.
Furthermore, engineers require a comprehensive overview of all network facets in the event of service disruptions such as troubleshooting voice calls. Other common disruptions can include cell congestion for both voice and data, high bandwidth users, and security threats like DDoS attacks.
However, finding the root cause of these issues can take hours to days, taking time away from engineers who should focus on problems with a more significant risk of disrupting end-user service. Thankfully, automation can take on tasks to optimize valuable employee time, especially in a reduced workforce. The most effective solutions can roll up the most common problems and place them in impact order. If there is a cell that is the common factor, it will roll to the top. This could be congestion or an error in configuration, so the most beneficial solution is one that can identify, drill down, and troubleshoot these issues automatically.
Automation dramatically reduces downtime that would otherwise occur as employees analyze and fix issues. Intelligent automation uses a framework that leverages packet data and combines AI/ML algorithms with domain expertise to identify the most business-impacting issues along with the initial point of failure and root causes of the problems. This approach can reveal hidden patterns and differentiate between relevant data and background noise. Identifying the cause of service assurance issues allows operators to see if they are related to the RAN, 5G core signaling, or if latency is introduced within the hyperscaler, etc.
When the root cause of issues can be identified, a more detailed investigation by response teams is enabled, helping triage and prioritize attention on the most important issues that warrant concentration with the necessary forensic evidence.
Achieve service assurance with end-to-end visibilityÂ
Lastly, CSPs must deploy monitoring solutions for 5G NSA or 5G SA network architectures. The foundation of any AI system is quality data. The best source of data about network activity is packet data. Carriers need first to ensure that they can monitor these networks to find success with the automation of their 5G SA networks.
When the framework leverages packet data, network operators can have high-resolution end-to-end network visibility that extends from the network’s edge to the core infrastructure. With real-time packet data analysis, CSPs can obtain complete visibility across all layers of the 5G network. This gives CSPs a high-fidelity, centralized view of the performance characteristics across the 5G infrastructure.
The transition to 5G leads to new complexities and is made even more challenging by the ongoing labor shortage in telecommunications. With automated tools that offer improved, end-to-end visibility across a network, CSPs can focus their attention – and their limited resources – on only the most important tasks.
