  In this paper, we investigate remote state estimation against an intelligent
denial-of-service (DoS) attack over a vulnerable wireless network whose channel
undergoes attenuation and distortion caused by fading. We use the sensor to
observe system states and transmit its local state estimates to the remote
center. Meanwhile, the attacker injects a jamming signal to destroy the packet
accepted by the remote center and causes the performance degradation. Most of
the existing works are built on a time-invariant channel state information
(CSI) model in which the channel fading is stationary. However, the wireless
communication channels are more prone to dynamic changes. To capture this
time-variant property in the channel quality of the real-world networks, we
study the fading channel network whose channel model is characterized by a
generalized finite-state Markov chain. With the goals of two players in
infinite-time horizon, we describe the conflicting characteristic between the
attacker and the sensor with a general-sum stochastic game. Moreover, the
Q-learning techniques are applied to obtain an optimal strategy pair at a Nash
equilibrium. Also the monotone structure of the optimal stationary strategy is
constructed under a sufficient condition. Besides, when channel gain is known a
priori, except for the full Channel State Information (CSI), we also
investigate the partial CSI, where Bayesian games are employed. Based on the
player's own channel information and the belief on the channel distribution of
other players, the energy strategy at a Nash equilibrium is obtained.
