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Network Architecture for Machine Learning : A Network Operator's Perspective

Data-driven network design suggests that substantial technology advances of 5G and 6G networks will be enabled with enhanced automation, intelligence, and user-experience-focused capabilities. Network operators need to upgrade the standard network models by applying machine learning (ML) to address the complexities of next-generation network deployments. This article explores the role of ML and its interplay with wireless communications networks to develop the next-generation network architecture. A use case scenario for self-configuration of radio-access-network-based notification areas (RNAs) for effective resource management is analyzed to exemplify the proposed architecture where a paging load reduction of 64 percent is observed in the resulting RNA clusters. A conceptual framework for RNA configuration and management enabling a broader perspective toward an ML-driven hybrid self-organizing network is discussed to improve the signaling load to attain reduced latency and improved network capacity.

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Informasi Detail

Judul Seri

IEEE COMMUNICATIONS MAGAZINE; Vol.60 No.7 July 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 68-74

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

COMPUTER ARCHITECTURE
ARTIFICIAL INTELLIGENCE
FEATURE EXTRACTION
NEXT GENERATION NETWORKING
NETWORK ARCHITECTURE
5G MOBILE COMMUNICATION
6G MOBILE COMMUNICATION

Info Detail Spesifik

DOI: 10.1109/MCOM.006.2100456

Pernyataan Tanggungjawab

Chetana V. Murudkar, Kwang-Cheng Chen, Richard D. Gitlin

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