Research On Caching Strategies Supporting D2D Communication Based On Reinforcement Learning

With the advent of the 5G era and the massive application of mobile devices and the Internet of Things,data traffic has exploded.How to offload traffic to a heterogeneous cellular network system while ensuring user Qo S and Qo E is a key point to be solved.The ultra-dense deployment of small base stations has become a solution,but the ultra-dense deployment of base stations is relatively expensive.To solve this problem,many researchers have proposed wireless edge caching solutions.Content caching at network nodes can effectively improve network performance and reduce backhaul link burden also reduces content transmission redundancy.Wireless edge caching generally includes two aspects: cache content placement and content delivery.Cache content placement includes determining the content to be cached and the location of the cache;content delivery is how to efficiently deliver content to the requesting user.In order to solve the above problems,the focus of this paper is to predict the user's mobility,content popularity and combine the user's social relationship and other information,so as to propose a reasonable content placement strategy and content delivery strategy.The main work of this paper are as follows:Firstly: Edge caching strategy based on the D2 D sharing model: Caching at the edge of the wireless network can greatly offload network traffic,while at the same time being able to satisfy local mobile users 'requests for content.However,in some existing studies,it is not strict to assume that the content popularity is equal to the user's preference.This is because the content popularity is calculated based on the statistical data requested by the user during a specific period,and the user's preference reflects the individual the probability of the content requested by the user.Therefore,this paper optimizes edge caching in D2 D and cellular layered wireless networks.The optimization result is to maximize the content offload through the D2 D communication link.Based on the analysis of content popularity,user preferences and user mobility,the edge caching strategy based on the D2 D sharing model is derived.First prove that the problem is an NP problem,and then use a distributed content replacement strategy based on Q-learning.Experimental simulation results prove the effectiveness of our algorithm.Secondly: Research on joint content placement and content delivery strategies based on D2 D networks: D2 D communication technology is expected to ease network pressure because requested content can be obtained from nearby users,but due to limited content storage capacity and uncertain user mobility patterns it is very challenging to design an effective caching strategy.In this paper,westudy strategies for joint cached content placement and content delivery based on D2 D networks.Specifically: using a recursive neural network method to predict user mobility and content popularity,so as to determine what to cache and where to cache.When it is difficult for a user's local cache to satisfy his own request,the user may consider establishing a D2 D link with a neighboring user to achieve content delivery.In order to decide which user to establish a D2 D link with,we adopt a deep reinforcement learning-based solution to implement dynamic decision-making and optimization of content delivery problems.Experimental results show that the proposed content placement strategy can improve the cache hit rate of the system,and the proposed content delivery method can effectively reduce the delay and energy consumption of requested content delivery.