AI in EE

AI IN DIVISIONS

AI in Communication Division

AI in EE

AI IN DIVISIONS

AI in Communication Division ​

AI in Communication Division

Channel Correlation in Multi-User Covert Communication: Friend or Foe? (하정석 교수 연구실)

Title: Channel Correlation in Multi-User Covert Communication: Friend or Foe?

Abstract:

In this work, we study a covert communication scheme in which some users are opportunistically selected to emit interference signals for the purpose of hiding the communication of a covert user. This work reveals interesting facts that the channel correlation is beneficial to the throughput of the covert communication but detrimental to the energy efficiency, which has never been discussed before. The study is conducted in a generic setup where the channels between pairs of entities in the scheme are correlated. For the setup, we discover that the optimal power profile of the interference signals from the selected users turns out to be the equal power transmission at their maximum transmit power level. In addition, we optimize system parameters of the scheme for maximizing throughput and energy efficiency utilizing Q-learning, which however is plagued with long learning time and large storage space when the dimension of state gets large and/or a fine resolution of reward function value is necessary. To resolve the technical challenge, we propose a scalable Q-learning which recursively narrows down the discretization level of the continuous state in an iterative fashion. To confirm the results in this work, the system parameters are evaluated with theoretical results for independent channels and compared with the ones from the proposed scalable Q-learning.

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