Abstract
The emerging mobile edge networks with content caching capability allows end users to receive information from adjacent edge servers directly instead of a centralized data warehouse, thus the network transmission delay and system throughput can be improved significantly. Since the duplicate content transmissions between edge network and remote cloud can be reduced, the appropriate caching strategy can also improve the system energy efficiency of mobile edge networks to a great extent. This paper focuses on how to improve the network energy efficiency and proposes an intelligent caching strategy according to the cached content distribution model for mobile edge networks based on promising deep reinforcement learning algorithm. The deep neural network (DNN) and Q-learning algorithm are combined to design a deep reinforcement learning framework named as the deep-Q neural network (DQN), in which the DNN is adopted to represent the approximation of action-state value function in the Q-learning solution. The parameters iteration strategies in the proposed DQN algorithm were improved through stochastic gradient descent method, so the DQN algorithm could converge to the optimal solution quickly, and the network performance of the content caching policy can be optimized. The simulation results show that the proposed intelligent DQN-based content cache strategy with enough training steps could improve the energy efficiency of the mobile edge networks significantly.
Highlights
With the rapid development of wireless communication networks and the fast growing of smart devices, various mobile Internet applications has been spawned, such as voice recognition, autonomous driving, virtual reality and augmented reality
We study a mobile edge network with unknown content popularity, and design a dynamic and smart content cache policy based on online learning algorithm which utilizes deep reinforcement learning (DRL)
The agent in our Reinforcement learning (RL) model is trained by deep-Q neural network (DQN) algorithm in this paper, the DQN algorithm uses two neural networks to realize the convergence of value function
Summary
With the rapid development of wireless communication networks and the fast growing of smart devices, various mobile Internet applications has been spawned, such as voice recognition, autonomous driving, virtual reality and augmented reality. These emerging services and applications put forward higher requirements for high capacity, low latency and low energy consumption. We design some numerical simulations and analyze the results, which show that the proposed strategy could greatly improve mobile edge networks energy efficiency without decreasing the system performance
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