Drone-Aided Network Coding for Secure Wireless Communications: A Reinforcement Learning Approach

This study investigates how base stations (BSs) apply network coding to protect the downlink data and how drones relay the coded packets to resist active eavesdropping that performs jamming to induce the BS to raise the transmit power and thus steal more data. We present a drone-aided network coding...

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Published in:2021 IEEE Global Communications Conference (GLOBECOM) pp. 01 - 06
Main Authors: Li, Hongyan, Yu, Shi, Lu, Xiaozhen, Xiao, Liang, Wang, Li-Chun
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2021
Subjects:
Base stations
Benchmark testing
drones
eavesdropping
Knowledge engineering
Network coding
rein-forcement learning
Reinforcement learning
Resists
Simulation
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Abstract This study investigates how base stations (BSs) apply network coding to protect the downlink data and how drones relay the coded packets to resist active eavesdropping that performs jamming to induce the BS to raise the transmit power and thus steal more data. We present a drone-aided network coding framework for secure downlink transmission, which incorporates a random linear network coding algorithm to encode the BS messages against active eavesdropping. This framework designs a model-based reinforcement learning to choose the BS network coding and transmission policy based on the jamming power sent by the active eavesdropper, the previous transmission performance, and the BS channel states without the prior knowledge of the drone-eavesdropper channel states. The learning parameters such as the Q-values are updated by the real experiences in the downlink transmission process besides the simulated experiences that are generated from the virtual model in the designed Dyna architecture. Simulation results show that our proposed scheme outperforms the benchmarks in terms of the intercept probability, the transmission performance, and the BS energy consumption.
AbstractList This study investigates how base stations (BSs) apply network coding to protect the downlink data and how drones relay the coded packets to resist active eavesdropping that performs jamming to induce the BS to raise the transmit power and thus steal more data. We present a drone-aided network coding framework for secure downlink transmission, which incorporates a random linear network coding algorithm to encode the BS messages against active eavesdropping. This framework designs a model-based reinforcement learning to choose the BS network coding and transmission policy based on the jamming power sent by the active eavesdropper, the previous transmission performance, and the BS channel states without the prior knowledge of the drone-eavesdropper channel states. The learning parameters such as the Q-values are updated by the real experiences in the downlink transmission process besides the simulated experiences that are generated from the virtual model in the designed Dyna architecture. Simulation results show that our proposed scheme outperforms the benchmarks in terms of the intercept probability, the transmission performance, and the BS energy consumption.
Author Xiao, Liang
Yu, Shi
Lu, Xiaozhen
Li, Hongyan
Wang, Li-Chun
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  email: lichun@g2.nctu.edu.tw
  organization: National Chiao Tung University,Dept. of Electrical Engineering & Computer Science
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Snippet This study investigates how base stations (BSs) apply network coding to protect the downlink data and how drones relay the coded packets to resist active...
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StartPage 01
SubjectTerms Base stations
Benchmark testing
drones
eavesdropping
Knowledge engineering
Network coding
rein-forcement learning
Reinforcement learning
Resists
Simulation
Title Drone-Aided Network Coding for Secure Wireless Communications: A Reinforcement Learning Approach
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