Insight Research writes for ToolBox: AI, Cloud, and Telcos – A Tantalizing Interplay

Published on September 18, 2020

Featured on Spiceworks, Kaustubha Parkhi, Principal Analyst at Insight Research, presents a thought-provoking article that explores the pivotal role of artificial intelligence (AI) in the design and management of cloud-native 5G Radio Access Networks (RAN). With the advent of 5G and cloud-native network functions (CNFs), AI's entry into real-time RAN algorithm management becomes increasingly important. This article delves into the interplay between CNFs and 5G, highlighting the transformative potential of AI in the telecommunications industry.

Key points discussed in the article:

  1. 5G Unleashed: As telcos prepare to fully harness 5G's potential, including standalone mode, new radio, and various use-cases, the network architecture must undergo a radical transformation to meet the agility demands of 5G.
  2. Cloud-Native Network Functions (CNFs): CNFs represent a significant advancement beyond virtualized network functions (VNFs) by embracing container technology, which brings profound changes to the world of network function engineering.
  3. Vendor Landscape: Established telco equipment vendors like Ericsson, Nokia, Huawei, and ZTE, as well as companies such as Mavenir, Athonet, Baicells, CCN, Phluido, and Quortus, are offering virtualized or cloud-native solutions. This shift challenges hardware-driven, proprietary RAN and packet core approaches.
  4. AI in 5G Networks: The article focuses on the role of AI in the core network architecture of cloud-native 5G, going beyond AI's impact on disparate applications. It explores AI's integration into the heart of cloud-native 5G networks.
  5. Cloud-Native 5G's Impact: Cloud-native 5G leverages containers, making it well-suited for AI and machine learning (ML) constructs. The article discusses how containers facilitate the distribution of AI/ML models and tuning updates seamlessly.
  6. AI at the Network Architecture Level: AI's potential extends to network planning, management, and troubleshooting, offering significant benefits in identifying anomalies and optimizing various network functions.
  7. Challenges of AI Integration: Challenges associated with integrating AI/ML with network functions, especially in CNFs, are addressed. The fine-grained nature of microservices and handling massive data volumes are key considerations.
  8. Validation and Testing: The article emphasizes the need for validation and testing methodologies to ensure that AI delivers promised results in RAN design and management.

AI is poised to play a pivotal role in the evolution of cloud-native 5G RAN, but its progress depends on the readiness of RAN datasets and a broader consensus on the framework for AI-friendly, cloud-native RANs.

Read the full article here:

For media inquiries or further information, please contact:

Manash Gogoi
Insight Research Corporation
Bengaluru, India

Select your currency
USD United States (US) dollar