Cache-enabled Wireless Networks with Opportunistic Interference Alignment

Abstract

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.

References in corpus (5)

• Feasibility Conditions for Interference Alignment
• Caching at the Wireless Edge: Design Aspects, Challenges and Future Directions
• From Spectrum Pooling to Space Pooling: Opportunistic Interference Alignment in MIMO Cognitive Networks
• Opportunistic Interference Alignment in MIMO Interference Channels
• Distributed Deep Q-Learning

Cited by in corpus (2)

• Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
• Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey