Geodesic
Deep Reinforcement Learning for Resource Allocation with Network Slicing in Cognitive Radio Network
Computer Science and Information Systems, Tome 18 (2021) no. 3.

Voir la notice de l'article provenant de la source Computer Science and Information Systems website

With the development of wireless communication technology, the requirement for data rate is growing rapidly. Mobile communication system faces the problem of shortage of spectrum resources. Cognitive radio technology allows secondary users to use the frequencies authorized to the primary user with the permission of the primary user, which can effectively improve the utilization of spectrum resources. In this article, we establish a cognitive network model based on underlay model and propose a cognitive network resource allocation algorithm based on DDQN (Double Deep Q Network). The algorithm jointly optimizes the spectrum efficiency of the cognitive network and QoE (Quality of Experience) of cognitive users through channel selection and power control of the cognitive users. Simulation results show that proposed algorithm can effectively improve the spectral efficiency and QoE. Compared with Q-learning and DQN, this algorithm can converge faster and obtain higher spectral efficiency and QoE. The algorithm shows a more stable and efficient performance.
Keywords: cognitive radio network, network slicing, resource allocation, deep reinforcement learning 
@article{CSIS_2021_18_3_a16,
     author = {Siyu Yuan and Yong Zhang and Wenbo Qie and Tengteng Ma and Sisi Li},
     title = {Deep {Reinforcement} {Learning} for {Resource} {Allocation} with {Network} {Slicing} in {Cognitive} {Radio} {Network}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {18},
     number = {3},
     year = {2021},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2021_18_3_a16/}
}
TY  - JOUR
AU  - Siyu Yuan
AU  - Yong Zhang
AU  - Wenbo Qie
AU  - Tengteng Ma
AU  - Sisi Li
TI  - Deep Reinforcement Learning for Resource Allocation with Network Slicing in Cognitive Radio Network
JO  - Computer Science and Information Systems
PY  - 2021
VL  - 18
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PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/CSIS_2021_18_3_a16/
ID  - CSIS_2021_18_3_a16
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%A Tengteng Ma
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%J Computer Science and Information Systems
%D 2021
%V 18
%N 3
%I mathdoc
%U http://geodesic.mathdoc.fr/item/CSIS_2021_18_3_a16/
%F CSIS_2021_18_3_a16
Siyu Yuan; Yong Zhang; Wenbo Qie; Tengteng Ma; Sisi Li. Deep Reinforcement Learning for Resource Allocation with Network Slicing in Cognitive Radio Network. Computer Science and Information Systems, Tome 18 (2021) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2021_18_3_a16/