How to Succeed in 5G Without Really Trying

As mobile networks expand from 4G to 5G, a brave new world of advanced capabilities is on the horizon. To support these new applications and services, radio access network (RAN) architectures will be under increased pressure to deliver the low latency and higher data throughputs required to assure seamless performance and exceptional customer service. Although leveraging artificial intelligence (AI) and machine learning (ML) analysis of next-gen RAN will be instrumental to achieving performance assurance, legacy datasets simply will not be sufficient to solve capacity and coverage issues or to enable automated intelligence because of the complexities involved.

Service Providers Face Changing Assurance Needs

In this new mobile environment, assurance needs are shifting. Technologies have changed with each mobile generation, presenting different requirements and capabilities. And with the rollout of 5G, communications service providers (CSPs) now face new assurance challenges.  

The industry has evolved, moving from embedded to independent and then to fully scalable systems. This began with embedded management systems, which led to click-through rate (CTR)-based instruments and apps and now is moving to fully independent brand analytics for 5G networks.

With the advent of 5G, the old reactive operational approach must give way to a more proactive one that is better suited to supporting decisions and assurance. This will require automated RAN-based decision-making to enable CSPs to optimize and configure cell coverage and characteristics. AI- and ML-driven analytics will be key to achieving this assurance and delivering the requisite business impact and optimization.

RAN Data Provides Intelligent Insights

The extensive volume of data collected from the RAN is crucial for powering the analytics needed to provide intelligent insights that ultimately support operations. Data gathered from 4G and 5G networks tends to be extremely diverse, encompassing subscriber information, performance across the network, and customer experience.

Information gathered on subscribers can determine if they are post-paid or prepaid as well as forecast potential churn. Operationally, this data can be used for anomaly detection, to reveal how individual network elements are functioning, and to identify where maintenance is required. RAN data also provides invaluable insights for dropped-call analysis to enable operational teams in more accurately diagnosing network issues and taking corrective actions.

The Importance of Accurate Datasets 

Generally, when information is collected from the RAN, it first must be prepared and then analyzed, which tends to be a complex and difficult process. To more effectively support today’s emerging 5G networks, automated systems that deliver intelligent RAN will be essential. How successful an ML automation model will be is dependent on the data that underpins the analysis.

Standard datasets simply won’t suffice for analyzing advanced RAN information. These datasets won’t provide the accuracy needed for the wide array of use cases involved. For instance, standard datasets won’t be able to solve critical issues related to the capacity of the RAN or coverage. It’s important to note that what works in 4G may not be relevant in advanced 5G networks. Legacy signaling datasets cannot simply correlate performance issues and service objectives. Highly accurate datasets will be necessary for automated analysis. Next-generation data sources that can effectively address complex use cases with automation will be the key to successful 5G performance assurance.

Watch our recent webinar The Secret Is Out: 5G Success with Intelligent Automation to learn more about next-gen datasets and how they are key to network assurance.