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.
@article{arxiv.1706.09024,
title = {Cache-enabled Wireless Networks with Opportunistic Interference Alignment},
author = {Y. He and S. Hu},
journal= {arXiv preprint arXiv:1706.09024},
year = {2017}
}