scholarly journals Efficient caching resource allocation for network slicing in 5G core network

2017 ◽  
Vol 11 (18) ◽  
pp. 2792-2799 ◽  
Author(s):  
Qingmin Jia ◽  
Renchao Xie ◽  
Tao Huang ◽  
Jiang Liu ◽  
Yunjie Liu
Keyword(s):  
Resource Allocation ◽  
Core Network ◽  
Network Slicing

scholarly journals Admission Control for 5G Network Slicing based on (Deep) Reinforcement Learning

2021 ◽  
Author(s):  
William Fernando Villota Jácome ◽  
Oscar Mauricio Caicedo Rendon ◽  
Nelson Luis Saldanha da Fonseca
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Admission Control ◽  
Time Windows ◽  
Core Network ◽  
Network Slicing ◽  
Q Learning ◽  
Model Free ◽  
5G Network ◽  
Substrate Network

Network Slicing is a promising technology for providing customized logical and virtualized networks for the industry’s vertical segments.This paper proposes SARA and DSARA for the performance of admission control and resource allocation for network slice requests of eMBB, URLLC, and MIoT type in the 5G core network. SARA introduced a Q-learning based algorithm and DSARA a DQN-based algorithm to select the most profitable requests from a set that arrived in given time windows. These algorithms are model-free, meaning they do not make assumptions about the substrate network as do optimization based approaches.

scholarly journals Admission Control for 5G Network Slicing based on (Deep) Reinforcement Learning

2021 ◽  
Author(s):  
William Fernando Villota Jácome ◽  
Oscar Mauricio Caicedo Rendon ◽  
Nelson Luis Saldanha da Fonseca
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Admission Control ◽  
Time Windows ◽  
Core Network ◽  
Network Slicing ◽  
Q Learning ◽  
Model Free ◽  
5G Network ◽  
Substrate Network

Network Slicing is a promising technology for providing customized logical and virtualized networks for the industry’s vertical segments.This paper proposes SARA and DSARA for the performance of admission control and resource allocation for network slice requests of eMBB, URLLC, and MIoT type in the 5G core network. SARA introduced a Q-learning based algorithm and DSARA a DQN-based algorithm to select the most profitable requests from a set that arrived in given time windows. These algorithms are model-free, meaning they do not make assumptions about the substrate network as do optimization based approaches.

Optimization of Spectrum Resource Allocation based on Network Slicing

2021 ◽  
Author(s):  
Cheng-Yu Chen ◽  
Pin-Rong Lin ◽  
Yu-Cheng Chen ◽  
Po-Hao Chang ◽  
Shiann-Shun Jeng
Keyword(s):  
Resource Allocation ◽  
Network Slicing
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