Upon 4G evolution, analytics by advanced AI/machine learning (ML) algorithms doing predictive analytics, anomaly detection, trend analysis, and clustering has become a priority for use cases such as customer experience management, personalized marketing or data monetization with network management. In addition of 5G and its difficulties, there is a stronger need for advanced prescriptive analytics to drive closed-loop automation and self-healing networks. So, now let us look into automating 5G with ML, DA & Reliable 5g tester, 5G test equipment, 5g network tester tools and Reliable RF drive test tools & equipment, RF tester software app & network LTE 4g tester.
Considering with all of these, a common pain point among communications service providers (CSPs) – how to Combine analytics into the network, presently the analytics are complicated from the many non-standardized interfaces and unchangeable data collection techniques among network vendors.
5G cellular networks has many new features compared to the legacy cellular networks, like Network Data Analytics Function (NWDAF), which highlights the network operators to either execute their own Machine Learning (ML) based data analytics methodologies or combine third-party solutions to their networks.
Technological developments in wireless cellular networking are expected to increase the number of customers and end-points significantly, resulting in the creation of very compound and busy networks. The standardization of the fourth generation (4G) cellular systems by the 3rd Generation Partnership Project (3GPP) highlighted users to reach hundreds of Mbps, by allowing us to access the applications requiring high data rates such as high-definition TV.
Before 3GPP introduced NWDAF for 5G cellular networks, AI/ML models were frequently used in wireless networking also in many other areas. With the need for Ultra-Reliable and Low Latency Communication (URLLC) and unmatched data traffic that increases exponentially, the use of AI/ML models in cellular networks has become a necessity. 3GPP could not overlook this requirement, and consequently introduced NWDAF to fulfil this requirement.
What is NWDAF? – Architecture, services and use cases
NWDAF characterized in 3GPP TS 29.520 consolidates standard interfacing from the service-based engineering to gather information by subscription or request model from other network functions and similar methods. This is often to deliver analytics functions within the network for automation or reporting, solving major custom interface or format challenges.
NWDAF is anticipated to have a distributed design giving analytics at the edge in real-time and, a central function for analytics which need central aggregation (e.g., service experience).3GPP TR 23.791 has presently listed the following formula-based/AI-ML analytics use cases for 5G using NWDAF:
- Load-level computation and expectation for a network slice instance
- Service encounter computation and forecast for an application/UE group
- Load analytics data and expectation for a particular NF
- Network load execution computation and future load prediction
- UE Anticipated behaviour prediction
- UE Unusual behavior/anomaly detection
- UE Mobility-related data and prediction
- UE Communication design prediction
- Congestion data – current and anticipated for a particular location
- Quality of benefit (QoS) maintainability which includes reporting and predicting QoS change
Intelligent networks based on the state-of-the-art AI/ML techniques. next-generation wireless technologies, including 5G, would require the support of extremely high data rates; thus, decision making in the new radio systems can benefit from ML techniques. They propose different ML techniques for different tasks in 5G networks including supervised learning-based methods for MIMO channel controlling, unsupervised learning for anomaly detection, and reinforcement learning for decision making under unknown network conditions. Yet, they do not investigate how ML would be used for network analytics in 5G.
Here are a few ways you’ll overcome these challenges and convey productive next-generation analytics with NWDAF:
- Mandate a distributed architecture for analytics as well, this decreases network transmission capacity overhead due to analytics and makes a difference real-time use cases by design.
- Ensure RFPs and your chosen sellers for network functions have, or plan to have, NWDAF support for collecting and receiving analytics services.
- Look for carrier-grade analytics arrangements with five nines’ SLAs.
- Choose modular analytics systems that can accommodate multiple use cases counting NWDAF as apps and support speedy development.
- Resource-efficient solutions are basic for on-premise or cloud as they can decrease costs considerably. Storage comes with a cost, store more processed smart data and not more raw huge data unless mandated by law.
- In designing analytics use case, get opinions from both telco and analytics experts, or ideally an expert in both, as they are viewed from different worlds and are evolving a lot.
In outline, a few works in the literature think about ML, AI, and deep learning (DL) concepts for 5G cellular systems with or without considering NWDAF. However, these works are right now immature. Moreover, even a few areas within the 3GPP specifications with respect to NWDAF are cleared out clear at the show due to the novelty of NWDAF.
CSPs are familiar with the benefits of utilizing analytics in telco systems, which include reducing operations and capital costs and creating modern income. As they move to 5G, analytics play an even bigger part beyond the conventional boundaries of telco systems counting radio access networks (RAN), and core operations/business support systems (OSS/BSS). Consequently, having a standards-defined NWDAF for the analytics needs of 5G, deployed with the proper scalable, optimized and distributed engineering, will simplify 5G/hybrid network sending and management and is critical to ensuring the very best client involvement.
