Research On Cooperative Caching In Fog-Ran

With the continuous and rapid proliferation of various intelligent devices and advanced mobile application services,wireless networks have been suffering an unprecedented data traffic pressure in recent years.Ever-increasing mobile data traffic brings tremendous pressure on capacity-limited fronthaul links.Fog radio access networks(F-RAN)as a promising architecture can effectively alleviate the traffic congestion in fronthaul links by placing popular contents at fog access points(F-APs)which are equipped with limited caching resources.Due to storage constraint and fluctuant spatio-temporal traffic demands,cooperative caching is an effective way to further increase the offloaded traffic.There are many problems required to solve,especially,how to divide the F-APs into clusters,how to eliminate the cache redundancy between cooperative F-APs,and how to make caching decisions cooperatively.To solve these problems,the thesis studies the cooperative caching in F-RAN.Firstly,the graph-based clustering and cooperative caching scheme is studied.To maximize the incremental offloaded traffic,we formulate the clustering optimization problem with the consideration of cooperative caching and local content popularity,which falls into the scope of combinatorial programming.We then propose an effective graph-based approach to solve this challenging problem.Firstly,a node graph is constructed with its vertex set representing the considered F-APs and its edge set reflecting the potential cooperations among the F-APs.Then,by exploiting the adjacency table,we propose to get the complete subgraphs through indirect searching for the maximal complete subgraphs.Furthermore,by using the complete subgraphs so obtained,a weighted graph is constructed.By setting the weights of the vertices of the weighted graph to be the incremental offloaded traffics of their corresponding complete subgraphs,the original clustering optimization problem can be transformed into an equivalent 0-1 integer programming problem.The max-weight independent subset of the vertex set of the weighted graph,which is equivalent to the objective cluster sets,can then be readily obtained by solving the above optimization problem through the greedy algorithm that we propose.Simulation results show the remarkable improvements in terms of offloading gain by using our proposed scheme.Secondly,the graph-based redundancy elimination cooperative caching scheme is studied.When utilizing the locally popular caching strategy,the whole offloaded traffic is restricted by the cache redundancy among cooperative F-APs.We formulate an optimization problem to minimize cache redundancy.By utilizing graph theory,we propose a graph-based approach.Firstly,a redundancy graph is constructed with its vertex set representing the considered F-APs and its edge set reflecting whether the corresponding cooperative F-APs have duplicate popular files or not.Then,the caching-decision correction indicator is used to control the caching location for each duplicate popular file at each edge.The purpose is to ensure each duplicate popular file is only cached once among the cooperative F-APs.During the control process,we always first check the popular files that can be fetched locally,so as to avoid repeated correction.The remaining storage space of each F-AP is filled by the unfetched files according to request probability.The computational complexity of the proposed approach is linear complexity.Simulation results show that cache redundancy elimination is helpful to improve the whole offloaded traffic.Finally,the graph-based cooperative content placement scheme is studied.The whole offloaded traffic is affected by the caching decisions of the requested F-APs and its cooperators.To maximize the whole offloaded traffic,we formulate a cooperative caching optimization problem.According to the relationship between clustering and cooperation,the whole offloaded traffic is rewritten as the sum of two parts.By exploiting the binary attribute of caching decisions,Lagrangian dual,and the termination criterion for iteration,we decompose the original problem into two subproblems.The first part offloaded traffic is affected by clustering strategy and caching decisions.By exploiting knapsack theory,we reformulate a clustering subproblem to maximize the first part offloaded traffic,which can be solved through the proposed graph-based clustering approach.The second part offloaded traffic is affected by the cache redundancy among inter-cluster cooperators and nonclustered cooperators.We reformulate a content placement subproblem to maximize the second part offloaded traffic.Since the duplicate popular files among inter-cluster cooperators and nonclustered cooperators are interconnected,we propose an improved graph-based redundancy elimination algorithm to solve the content placement subproblem.Simulation results show the remarkable offloaded traffic by using our proposed scheme.And it also reveals that both clustering and redundancy elimination are necessary for cooperative caching.