  Both caching and interference alignment (IA) are promising techniques for
future wireless networks. Nevertheless, most of existing works on cache-enabled
IA wireless networks assume that the channel is invariant, which is unrealistic
considering the time-varying nature of practical wireless environments. In this
paper, we consider realistic time-varying channels. Specifically, the channel
is formulated as a finite-state Markov channel (FSMC). The complexity of the
system is very high when we consider realistic FSMC models. Therefore, we
propose a novel big data reinforcement learning approach in this paper. Deep
reinforcement learning is an advanced reinforcement learning algorithm that
uses deep $Q$ network to approximate the $Q$ value-action function. Deep
reinforcement learning is used in this paper to obtain the optimal IA user
selection policy in cache-enabled opportunistic IA wireless networks.
Simulation results are presented to show the effectiveness of the proposed
scheme.
