Q-Learning Based Physical-Layer Secure Game Against Multiagent Attacks

In this paper, we consider a Q-learning-based power allocation strategy for a secure physical-layer system under dynamic radio environments. In such a system, the transmitter sends the information to the receiver threatened by <inline-formula> <tex-math notation="LaTeX">${M (M \geq 2)}$ </tex-math></inline-formula> intelligent attackers which have several attack modes and will bring out the severe issue of information security. To safeguard the system security, we formulate the insecure problem as a stochastic game which consists of <inline-formula> <tex-math notation="LaTeX">${M+1}$ </tex-math></inline-formula> players: the transmitter which can flexibly choose its transmit power, and <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> smart attackers that can determine their attack types. Then, the Nash equilibria (NEs) of the physical-layer secure game are derived, and their existence conditions are taken into account. The simulation results show that the proposed power allocation strategy in the stochastic game can efficiently suppress the attack rate of smart attackers even if there exist multiple smart attackers.