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5G will come with opportunities for growth but will have its own challenges. CSPs must therefore be proactive to understand these challenges and how they can be tackled, otherwise risk delivering value to customers and stakeholders. Omdia’s latest report The Role of AI in addressing 5G challenges identifies the challenges that CSPs will encounter with 5G, the role that AI can play in addressing these challenges and how CSPs can prepare themselves to take advantage of AI to improve their 5G networks.


CSPs should invest in AI technologies to secure their 5G investments

As CSPs rollout 5G networks, they should expect increased complexity when operating these networks. This complexity will be driven mainly by the co-existence of 5G networks with legacy networks. Given most CSPs do not plan to phase out legacy networks, deploying these 5G networks will add more challenges to operating the overall network infrastructure.

This complexity will span both network and service domains. Regarding the networks, the introduction of technologies such as network slicing, massive multiple input, multiple output (mMIMO) antennas, software defined networking (SDN) and network functions virtualization (NFV) will complicate the operations of the network. With the broader spectrum of services enabled by 5G, we’ll see an increase in the number of devices and services to be monitored and assured.

On the other hand, 5G network traffic will generate large volumes of data. Relying on traditional analytics procedures to meet customer requirements and operational objectives will not be feasible. The scale of the challenge is too broad for these analytics solutions to address.

Adopting AI technologies can however address these challenges. The real time intelligence, prediction, and decision-making capabilities of these technologies can be implemented within 5G network to drive efficient operations, optimize costs and improve customer efficiency. Application of AI tools can enable faster analysis of network data to understand user, service, and device trends and consumption patterns, traffic forecasting to support network planning, optimization, and management, and facilitate decision making to accelerate root cause analysis and resolution.

AI capabilities can also be applied to the management of the new technologies that come with 5G; including massive MIMO, mobile edge computing, and network slicing. End-to-end network slicing management can be achieved leveraging AI techniques like machine learning to analyze incoming network traffic and predict service demand. Based on this insight, the AI system can recommend an optimal network slicing model and orchestration policies required to support the services. Another example can be seen in the operations of mMIMO antenna. ML through the analysis of historical and current data can generate a model that simulates the distribution of users and based on this model forecast the distribution of users. Optimal weights of the antenna elements that satisfy the forecasted distribution can then be generated. The re-adjustment of these weights relative to traffic distribution can also be achieved using ML.

To take advantage of these applications, CSPs must align their AI implementation strategy with 5G deployment strategy, build a clear data strategy for AI initiatives to support 5G deployments, and invest in the right skills to develop and manage AI models. Use cases deployed must align with the business objectives for 5G. Furthermore, given how distributed the CSP network environment is, AI will need to be deployed either locally, within the network equipment or network edge or centrally for network control and management. Consequently, CSPs’ network data strategy must take into consideration how to collect, process, and analyze data based on these different modes. Interoperability and openness of the network infrastructure must also be addressed as it’s critical that data sets needed to drive AI workflows can be accessed when and where required. Employee skillset must also be updated to ensure AI models remain relevant to the network issues they were designed to address. Taking these factors into consideration will ensure AI effectively addresses the challenges that will come with 5G and ensure CSPs derive quicker returns on investments (ROI).