Joint Design of Content Delivery and Recommendation in Wireless Caching Networks

Although content caching and recommendation are two complementary approaches to improve the user experience,it is still challenging to provide an integrated paradigm to fully explore their potential,due to the high complexity and complicated tradeoff relationship.To provide an efficient management framework,the joint design of content delivery and recommendation in wireless content caching networks is studied in this paper.First,a joint transmission scheme of content objects and recommendation lists is designed with edge caching,and an optimization problem is formulated to balance the utility and cost of content caching and recommendation,which is an mixed integer nonlinear programming problem.Second,a reinforcement learning based algorithm is proposed to implement real time management of content caching,recommendation and delivery,which can approach the optimal solution without iterations during each decision epoch.Finally,the simulation results are provided to evaluate the performance of our proposed scheme,which show that it can achieve lower cost than the existing content caching and recommendation schemes.

Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

With the development of internet of vehicles,the traditional centralized content caching mode transmits content through the core network,which causes a large delay and cannot meet the demands for delay-sensitive services.To solve these problems,on basis of vehicle caching network,we propose an edge colla-borative caching scheme.Road side unit(RSU)and mobile edge computing(MEC)are used to collect vehicle information,predict and cache popular content,thereby provide low-latency content delivery services.However,the storage capa-city of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time.Through content sharing,col-laborative caching can relieve the storage burden on caching servers.Therefore,we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching(MVECC)scheme,so as to realize the collaborative caching among cloud,edge and vehicle.MVECC uses deep reinforcement learning to pre-dict what needs to be cached on RSU,which enables RSUs to cache more popular content.In addition,MVECC also introduces a mobility-aware caching replace-ment scheme at the edge network to reduce redundant cache and improving cache efficiency,which allows RSU to dynamically replace the cached content in response to the mobility of vehicles.The simulation results show that the pro-posed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.

Cellular traffic offloading utilizing set-cover based caching in mobile social networks

To cope with the explosive data demands, offloading cellular traffic through mobile social networks（MSNs） has become a promising approach to alleviate traffic load. Indeed, the repeated data transmission results in a great deal of unnecessary traffic. Existing solutions generally adopt proactive caching and achieve traffic shifting by exploiting opportunistic contacts. The key challenge to maximize the offloading utility needs leveraging the trade-off between the offloaded traffic and the users＇ delay requirement. Since current caching scheme rarely address this challenge, in this paper, we first quantitatively interpret the offloading revenues on the cellular operator side associated with the scale of caching users, then develop a centralized caching protocol to maximize the offloading revenues, which includes the selective algorithm of caching location based on set-cover, the cached-data dissemination strategy based on multi-path routing and the cache replacement policy based on data popularity. The experimental results on real-world mobility traces show that the proposed caching protocol outperforms existing schemes in offloading scenario.

Joint Caching and User Association for Energy Harvesting Aided Internet of Things with Full-Duplex Backhauls

Due to its wide coverage,stable link,and low latency,cellular network has become one of the main access networks for Internet of things(IoT).However,the expenses of backhauls and node charging grow fast with the number of nodes.Towards this issue,this paper addresses the joint caching and user association for energy harvesting aided IoT with full-duplex backhauls.We formulate the node charging,full-duplex base station association,and cache allocation by using Stackelberg game.Then the structural characteristics are utilized to decompose the model into a two-layer knapsack problem.On the basis of that,an alternative direction iteration is proposed,which firstly compresses the solution space by the constraints,and then obtains the optimization results by alternative iterations.Simulation results verify the effectiveness of the proposed algorithm in utility improvement and cost reduction.