Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks
Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power alloca...
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Online Access: | https://doi.org/10.1186/s13638-019-1595-x |
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doaj-30968c9c8a6245b7bfa96599490796d92021-01-10T12:43:50ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992020-01-01202011910.1186/s13638-019-1595-xCache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networksChao Li0Zihe Gao1Junjuan Xia2Dan Deng3Liseng Fan4The School of Computer Science, Guangzhou UniversityThe Research Center of Institute of Telecommunication Satellite, China Academy of Space TechnologyThe School of Computer Science, Guangzhou UniversityGuangzhou Panyu PolytechnicThe School of Computer Science, Guangzhou UniversityAbstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy.https://doi.org/10.1186/s13638-019-1595-xCacheUAVB5GNOMAPhysical-layer securityReinforcement learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chao Li Zihe Gao Junjuan Xia Dan Deng Liseng Fan |
spellingShingle |
Chao Li Zihe Gao Junjuan Xia Dan Deng Liseng Fan Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks EURASIP Journal on Wireless Communications and Networking Cache UAV B5G NOMA Physical-layer security Reinforcement learning |
author_facet |
Chao Li Zihe Gao Junjuan Xia Dan Deng Liseng Fan |
author_sort |
Chao Li |
title |
Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks |
title_short |
Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks |
title_full |
Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks |
title_fullStr |
Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks |
title_full_unstemmed |
Cache-enabled physical-layer secure game against smart uAV-assisted attacks in b5G NOMA networks |
title_sort |
cache-enabled physical-layer secure game against smart uav-assisted attacks in b5g noma networks |
publisher |
SpringerOpen |
series |
EURASIP Journal on Wireless Communications and Networking |
issn |
1687-1499 |
publishDate |
2020-01-01 |
description |
Abstract This paper investigates cache-enabled physical-layer secure communication in a no-orthogonal multiple access (NOMA) network with two users, where an intelligent unmanned aerial vehicle (UAV) is equipped with attack module which can perform as multiple attack modes. We present a power allocation strategy to enhance the transmission security. To this end, we propose an algorithm which can adaptively control the power allocation factor for the source station in NOMA network based on reinforcement learning. The interaction between the source station and UAV is regarded as a dynamic game. In the process of the game, the source station adjusts the power allocation factor appropriately according to the current work mode of the attack module on UAV. To maximize the benefit value, the source station keeps exploring the changing radio environment until the Nash equilibrium (NE) is reached. Moreover, the proof of the NE is given to verify the strategy we proposed is optimal. Simulation results prove the effectiveness of the strategy. |
topic |
Cache UAV B5G NOMA Physical-layer security Reinforcement learning |
url |
https://doi.org/10.1186/s13638-019-1595-x |
work_keys_str_mv |
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