Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks

TLDR

Caching and interference alignment are promising for next‑generation wireless networks, yet most studies assume invariant channels, ignoring the high complexity of realistic time‑varying finite‑state Markov channel models. The study aims to address realistic time‑varying channels by proposing a deep reinforcement learning approach using a deep Q‑network to optimize IA user selection. The authors model the channel as a finite‑state Markov channel and implement a deep Q‑network via TensorFlow to learn an optimal IA user‑selection policy for cache‑enabled opportunistic IA networks. Simulations demonstrate that the proposed deep‑reinforcement‑learning method significantly improves sum rate and energy efficiency in cache‑enabled opportunistic IA networks.

Abstract

Both caching and interference alignment (IA) are promising techniques for next-generation wireless networks. Nevertheless, most of the 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, in this paper, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses a deep Q network to approximate the Q value-action function. We use Google TensorFlow to implement deep reinforcement learning in this paper to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network's sum rate and energy efficiency can be significantly improved by using the proposed approach.