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Submission: On November 21 via manual from US — Scanned from DE
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IEEE websites place cookies on your device to give you the best user experience. By using our websites, you agree to the placement of these cookies. To learn more, read our Privacy Policy. Accept & Close A 6G RAN-Core Control Plane Convergence Framework | IEEE Conference Publication | IEEE Xplore Skip to Main Content * IEEE.org * IEEE Xplore * IEEE SA * IEEE Spectrum * More Sites Subscribe * Subscribe * Cart * * * Create Account * Personal Sign In * Browse * My Settings * Help Institutional Sign In Institutional Sign In AllBooksConferencesCoursesJournals & MagazinesStandardsAuthorsCitations ADVANCED SEARCH Conferences >2023 26th Conference on Innov... A 6G RAN-CORE CONTROL PLANE CONVERGENCE FRAMEWORK Publisher: IEEE Cite This PDF Marius Corici; Fabian Eichhorn; Eric Troudt; Florian Schreiner; Thomas Magedanz All Authors Sign In or Purchase to View Full Text 1 Cites in Paper 339 Full Text Views * * * * * Alerts ALERTS Manage Content Alerts Add to Citation Alerts -------------------------------------------------------------------------------- Abstract Document Sections * I. Introduction * II. 6G Dense Wireless Environment Requirments and background * III. RAN-Core Convergence Architecture * IV. Comparison: 5G vs. RAN-Core * V. Conclusions And Further Work Authors Figures References Citations Keywords Metrics More Like This * Download PDF * Download References * * Request Permissions * Save to * Alerts Abstract:With the centralization of the RAN control functionality and with the pushing of Core Network functionality towards the network edge, the thus far very strong separation ...View more Metadata Abstract: With the centralization of the RAN control functionality and with the pushing of Core Network functionality towards the network edge, the thus far very strong separation of concerns between RAN and core cannot be assumed any longer. Instead, almost the same network functionality for subscriber mobility and session management is placed in the same local aggregation point. This article presents a new architecture framework converging this functionality now planned for both RAN and Core Network within a single set of network functions, as well as their integration with the rest of the network components. Furthermore, the proposed new architecture is compared with the current 5G architecture and assessed against the 6G requirements, showing how it outperforms in terms of increased deployment specific customization flexibility and open interfaces. Published in: 2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Date of Conference: 06-09 March 2023 Date Added to IEEE Xplore: 22 March 2023 ISBN Information: ISSN Information: INSPEC Accession Number: 22815335 DOI: 10.1109/ICIN56760.2023.10073488 Publisher: IEEE Conference Location: Paris, France Funding Agency: Contents -------------------------------------------------------------------------------- I. INTRODUCTION To enable high-capacity service, a 5G Radio Access Network (RAN) deployment can utilize a set of Remote Radio Heads (RRHs) placed at different locations. The RRHs are connected with optic fiber connections to a radio baseband Central Unit (CU) which controls the RRHs and aggregates the communication at different levels [1] [2] . In dense urban or indoor environments, the devices have more connectivity options within the same area. The cell selection is not dependent on service continuity anymore. Instead, a scheduling of the devices across multiple RRHs, considering other factors such as the capacity consumed would create a more effective environment, especially given the also increasing number of connected devices. Sign in to Continue Reading Authors Figures References Citations Keywords Metrics More Like This Location and Mobility Aware Resource Management for 5G Cloud Radio Access Networks 2017 International Conference on High Performance Computing & Simulation (HPCS) Published: 2017 Data-Driven Estimation of Throughput Performance in Sliced Radio Access Networks via Supervised Learning IEEE Transactions on Network and Service Management Published: 2023 Show More REFERENCES References is not available for this document. 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