Communication networks are rapidly transforming from reactive systems into autonomous, intent-driven cognitive engines that match machine learning with software-based monitoring and operation.
The ITU Journal on Future and Evolving Technologies continues its extensive, in-depth coverage of machine learning for 5G and future networks.
The latest issue (Volume 6, issue 4) explores traffic forecasting, intent-driven automation, reinforcement learning for resource allocation, and small language models for standardization.
The issue shares solutions from ITU’s Global Challenge on machine learning for 5G and future networks.
It also includes papers on spectral efficiency analysis for 6G waveforms and economic perspectives on granular energy data.
The online ITU Journal – free of charge to readers and contributors – offers comprehensive coverage of communications and networking.
It welcomes research submissions all year long, on all topics relevant to the work of the International Telecommunication Union (ITU).
Published in open access format since 2020, the ITU Journal is indexed in the Directory of Open Access Journals.
