Abstract
An energy harvesting wiretap channel model is considered in which the sender is an energy harvesting node. It is assumed that at each time slot only information about the current state of the sending node is available. In order to find an effective power allocation strategy to maximize secrecy rate, we put forward a deep Q network (DQN) scheme. First, we analyze the constraints of the system and the issue of maximizing the secrecy rate. Next, the power allocation problem is formulated as a Markov Decision Process (MDP) with unknown transition probabilities. In order to solve the continuous state space problem that traditional Q learning algorithms cannot handle, we apply neural networks to approximate the value function. Finally, an online joint resource power allocation algorithm based on DQN is presented. Simulation results show that the proposed algorithm can effectively improve the secrecy rate of the model.
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Acknowledgement
This paper is sponsored by the National Nature Science Foundation of China (61971080, 61471076); Chongqing Basic Research and Frontier Exploration Project (cstc2018jcyjAX0432, cstc2017jcyjAX0204); The Key Project of Science and Technology Research of Chongqing Education Commission (KJZD-K201800603).
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Li, Z., Lei, W. (2020). Deep Q Network for Wiretap Channel Model with Energy Harvesting. In: Gao, H., Feng, Z., Yu, J., Wu, J. (eds) Communications and Networking. ChinaCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-41114-5_32
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DOI: https://doi.org/10.1007/978-3-030-41114-5_32
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