Three new deliverables from the 5G-Transformer project are available for download: –D1.2 5G-TRANSFORMER initial system design
–D.2.1 Initial design of 5G Coral edge and fog computing system
5G-XCast releases its D4.1 on Mobile Core Networks in June 2018. The document describes the 5G-Xcast mobile core network architecture, which enables multicast and broadcast capabilities based on 5G architecture defined in 3GPP Release 15. The 5G-Xcast mobile core network architecture considers the new functionalities and technologies such as converged autonomous switch between unicast, multicast and broadcast for the converged network including fixed broadband and mobile networks, multimedia public warning alert, multi-connectivity and multilink, and multi-access edge computing. This document describes the 5G-Xcast design principles that enables multicast and broadcast capabilities. These design principles are aligned with the ones identified in 3GPP Release 15. Focusing on multicast and broadcast capabilities in the mobile core network, D4.1 provides two different approaches to the same problem of network resource optimization: the transparent multicast transport and point-to-multipoint services including multicast, broadcast and terrestrial broadcast. Most importantly, the document describes two primary architecture alternatives to enable multicast and broadcast capabilities inside 5G core network architecture. Read more here.
The SliceNet logical architecture is defined as a Software Based Architecture approach, including several key elements in the 5G ecosystem, with various general functions as those provided by the Management Plane, Control Plane and Data Plane resources. See the image on the SliceNet logical architecture. SliceNet envisions an intelligent cost-effective network management, control, and orchestrations framework that can cope with the scale and pervasiveness of 5G networks, while minimizing human intervention. Autonomous management and control is evolving from self-healing, self-optimizing and self-configuring automation to artificially intelligent systems that can outlearn their current knowledge and apply newly gained wisdom to achieve network goals through self-decision-making.
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|