Research On Wireless Long-tail Caching Algorithm For Double-selective Fading Channels Based On Reinforcement Learnin

In the era of mobile internet,with the explosive growth of mobile devices,there is an increasing demand for mobile data services,such as low-latency and high-bandwidth wireless communication networks for services ranging from streaming to virtual reality technologies.However,the limited bandwidth and capacity of wireless networks cause latency and quality problems when transmitting data,which can affect the user experience.To solve these problems,wireless caching technology has emerged,which improves the efficiency and quality of data transmission by deploying data closer to users,thereby enhancing the user experience.In addition,using wireless caching technology can effectively reduce the transmission flow of the feedback link,lower the load on the central server,and enable more users to access the network.The content deployment issue of wireless caching is a challenging optimization problem because internet traffic follows a long-tail distribution,and only a few popular files occupy most of the internet traffic.Furthermore,wireless communication environments are affected by various complex factors,such as communication network topology,user mobility,and time-varying channels.With the increase in base station storage capacity and the shortening of network communication distance,base stations or user devices in the same network area can share their wireless caching content for cooperation.However,traditional convex optimization algorithms find it difficult to solve the caching strategy under various constraints.Therefore,this article aims to use reinforcement learning technology to study the caching deployment strategy of wireless caching networks with a long-tail distribution.Firstly,a deep reinforcement learning technology,based on reference to deep long-tail learning,is proposed to solve the content deployment problem in wireless cooperative caching networks.As base stations become more densely deployed,there is more cooperation between base stations.Through cooperation,the wireless caching between base stations can be further expanded through a cooperative link,and at the same time,the solution set of caching strategies is also expanded.Traditional convex optimization algorithms find it difficult to solve the problem under multiple constraints such as cooperation mechanism,single base station caching capacity limitation,and return traffic control.Therefore,this article proposes an anti-long-tail content caching algorithm based on reinforcement learning,which includes anti-long-tail distribution design from the design of state,action,and reward in reinforcement learning to the architecture of top-level neural networks.Compared with the depth reinforcement learning scheme,the proposed algorithm can effectively reduce user access latency and improve caching hit rate.Secondly,this article proposes a dual-selective fading channel caching algorithm design based on meta-reinforcement learning.As the frequency of new-generation mobile communication carriers increases,the Doppler frequency shift caused by user mobility will be further amplified,and the channel coherence time will be further shortened.To deploy wireless caching closer to users,it is necessary to quickly identify popular files and user channel conditions in a small number of user requests to solve caching strategies quickly.This article converts the optimization problem into a multidimensional knapsack problem through problem modeling to demonstrate that the wireless caching problem under dual fading channels is difficult to solve using traditional convex optimization algorithms.Therefore,this article considers using meta-reinforcement learning methods to reduce the coupling between different wireless caching scenarios.