Journal
PROCEEDINGS OF THE 2020 IEEE 45TH CONFERENCE ON LOCAL COMPUTER NETWORKS (LCN 2020)
Volume -, Issue -, Pages 286-296Publisher
IEEE COMPUTER SOC
DOI: 10.1109/LCN48667.2020.9314857
Keywords
5G RAN Slicing; 3GPP Functional Split; Radio Resource Allocation; uRLLC; eMBB; Machine Learning
Categories
Funding
- FUI SCORPION project [17/00464]
- CNRS PRESS project [239953]
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Network slicing is proposed as a new paradigm to serve the plethora of SG services on a shared infrastructure. Within this context, a Radio Access Network (RAN) slice is considered as the proportion of physical spectrum resources to be served to third parties. Interestingly, 3GPP standardized options of RAN processing dis-aggregation into network functions while enabling their placement whether in distributed or centralized locations. The adoption of an end-to-end RAN slicing raises new challenges related to the allocation efficiency of joint radio, link and computational resources. To deal with the stringent latency requirements of SG services, we propose, in this paper, a Deep Learning based approach for User-centric end-to-end RAN Slice Allocation scheme. It can decide in real-time, to jointly allocate the amount of radio resources and functional split for each end-user. Our proposal satisfies end-user's requirements in terms of throughput and latency, while minimizing the infrastructure deployment cost.
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