Today for Communication Service Providers (CSPs), moving to 5G is quickly becoming a necessity. Consequentially, this has led to a need for CSPs to ensure that they are well equipped to cover both expectations and the customer needs that 5G promises.
5G comes with a host of new services and business models. For CSPs to capitalize on the 5G opportunity and ensure a more significant market share, it would be important to understand how their networks will need to evolve to meet the rise in demand and traffic. At the same time, CSPs continue to amortize their 4G deployments, and this has made it paramount that customer experience on 4G is not affected as CSPs move closer to a 5G deployment.
The above elements represent a significant challenge for all different departments involved in the 5G deployment. The Radio Optimization team is probably the most impacted one, as they need to address several aspects, such as:
- Accelerating the learning phase with respect to 5G technology
- Maintaining QoE and QoS during the new technology integration, while switching off frequency carriers from legacy technologies, or even switching off a specific legacy technology (2G or 3G) in order to make most of the new spectrum scenario that comes with 5G
- Releasing new services to support new business needs: Fixed Wireless Access, Massive IoT, LTE Advanced, Massive MIMO, Network Slicing, Private Networks, etc.
Today, most of the optimization processes implemented consider data from PM Counters, Call Traces, Probes, Crowdsourcing solutions, Drive tests, etc. Hence, network KPIs built on data for network performance, CX, and network quality are generally used to provide insights to organize and prioritize actions, such as geolocation data, VIP Subscribers, Roamers, etc. However, these earlier methodologies, which were used to perform radio optimization for legacy technologies, are no longer sufficient to cater to all the needs indicated above and the new use cases that come with 5G.
To explain why we need to take a step back.
One of the main advantages 5G offers comes from introducing the possibility to create several tailored use cases that will open doors for newer revenue streams. However, managing the capacity to control the multiple performance indicators inherent to these new services will be significantly complex, as each particular use case will need very specific KPIs and SLAs to be monitored to maintain the high performance; KPIs which are not covered as part of the above-mentioned network KPIs.
Moreover, 5G will also facilitate network slices for different services or use cases, which will call for adding new analytics techniques and leveraging new data sources to assure a seamless customer experience, enhance profitability, and gain a competitive advantage.
For these reasons, it will be necessary for CSPs to maximize automation as much as possible to ensure RAN optimization for 5G. Here is where it will be important for CSPs to couple network KPIs with business data to prioritize and enhance the necessary optimization actions to meet the business needs as well as forecast and address the demand for new services built on 5G.
Generating holistic, actionable insights for improved decision making is only possible by correlating and enriching data from the different areas (Network, Finance, and Customer Experience), applying advanced machine learning techniques, defining the rules engine, and leveraging ML/DL models under the expertise of both data-scientists and domain experts.
Business data-driven optimization will ensure that radio teams focus their efforts towards maintaining high levels of QoE, QoS and CX for the most relevant revenue streams. Adopting an intelligent approach to RAN optimization can ensure that the ROI from different network elements can be easily tracked, managed and augmented. This can help CSPs ensure that their network Capex is optimized while bringing in a significant reduction in operational costs for RAN Optimization.
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