Reinforcement Learning Based Network Coding for Drone-Aided Secure Wireless Communications

Active eavesdropper sends jamming signals to raise the transmit power of base stations and steal more information from cellular systems. Network coding resists the active eavesdroppers that cannot obtain all the data flows, but highly relies on the wiretap channel states that are rarely known in wir...

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Vydané v:	IEEE transactions on communications Ročník 70; číslo 9; s. 5975 - 5988
Hlavní autori:	Xiao, Liang, Li, Hongyan, Yu, Shi, Zhang, Yi, Wang, Li-Chun, Ma, Shaodan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Cellular communication Coding Drones Eavesdropping Energy consumption Jamming Learning Network coding Network latency Neural networks Optimization Radio equipment reinforcement learning Relays Resource management secure communications Wireless communications Wireless networks Wiretapping
ISSN:	0090-6778, 1558-0857
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Abstract	Active eavesdropper sends jamming signals to raise the transmit power of base stations and steal more information from cellular systems. Network coding resists the active eavesdroppers that cannot obtain all the data flows, but highly relies on the wiretap channel states that are rarely known in wireless networks. In this paper, we present a reinforcement learning (RL) based random linear network coding scheme for drone-aided cellular systems to address eavesdropping. In this scheme, the network coding policy, including the encoded packet number, the packet and power allocation, is chosen based on the measured jamming power, previous transmission performance and BS channel states. A virtual model generates simulated experiences to update Q-values besides real experiences for faster policy optimization. We also propose a deep RL version and design a hierarchical architecture to further accelerate the policy exploration and improve the anti-eavesdropping performance, in terms of the intercept probability, the latency, the outage probability and the energy consumption. We analyze the computational complexity, drone deployment, secure coverage area and the performance bound of the proposed schemes, which are verified via simulation results.
AbstractList	Active eavesdropper sends jamming signals to raise the transmit power of base stations and steal more information from cellular systems. Network coding resists the active eavesdroppers that cannot obtain all the data flows, but highly relies on the wiretap channel states that are rarely known in wireless networks. In this paper, we present a reinforcement learning (RL) based random linear network coding scheme for drone-aided cellular systems to address eavesdropping. In this scheme, the network coding policy, including the encoded packet number, the packet and power allocation, is chosen based on the measured jamming power, previous transmission performance and BS channel states. A virtual model generates simulated experiences to update Q-values besides real experiences for faster policy optimization. We also propose a deep RL version and design a hierarchical architecture to further accelerate the policy exploration and improve the anti-eavesdropping performance, in terms of the intercept probability, the latency, the outage probability and the energy consumption. We analyze the computational complexity, drone deployment, secure coverage area and the performance bound of the proposed schemes, which are verified via simulation results.
Author	Xiao, Liang Ma, Shaodan Yu, Shi Zhang, Yi Li, Hongyan Wang, Li-Chun
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Snippet	Active eavesdropper sends jamming signals to raise the transmit power of base stations and steal more information from cellular systems. Network coding resists...
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SubjectTerms	Cellular communication Coding Drones Eavesdropping Energy consumption Jamming Learning Network coding Network latency Neural networks Optimization Radio equipment reinforcement learning Relays Resource management secure communications Wireless communications Wireless networks Wiretapping
Title	Reinforcement Learning Based Network Coding for Drone-Aided Secure Wireless Communications
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