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Register a new userCooperative Caching in Fog Radio Access Networks: A Graph-based Approach
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Yanxiang Jiang
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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In this paper, cooperative caching is investigated in fog radio access networks (F-RAN). To maximize the offloaded traffic, cooperative caching optimization problem is formulated. By analyzing the relationship between clustering and cooperation and utilizing the solutions of the knapsack problems, the above challenging optimization problem is transformed into a clustering subproblem and a content placement subproblem. To further reduce complexity, we propose an effective graph-based approach to solve the two subproblems. In the graph-based clustering approach, a node graph and a weighted graph are constructed. By setting the weights of the vertices of the weighted graph to be the incremental offloaded traffics of their corresponding complete subgraphs, the objective cluster sets can be readily obtained by using an effective greedy algorithm to search for the max-weight independent subset. In the graph-based content placement approach, a redundancy graph is constructed by removing the edges in the complete subgraphs of the node graph corresponding to the obtained cluster sets. Furthermore, we enhance the caching decisions to ensure each duplicate file is cached only once. Compared with traditional approximate solutions, our proposed graph-based approach has lower complexity. Simulation results show remarkable improvements in terms of offloaded traffic by using our proposed approach.
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In this paper, the cooperative caching problem in fog radio access networks (F-RAN) is investigated. 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. % and is NP-hard. 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 fog access points (F-APs) and its edge set reflecting the potential cooperations among the F-APs. %whether the F-APs the distance and load difference among the F-APs. Then, by exploiting the adjacency table of each vertex of the node graph, we propose to get the complete subgraphs through indirect searching for the maximal complete subgraphs for the sake of a reduced searching complexity. 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. Our proposed graph-based approach has an apparently low complexity in comparison with the brute force approach which has an exponential complexity. Simulation results show the remarkable improvements in terms of offloading gain by using our proposed approach.
This work studies the advantages of coded multicasting for the downlink of a Fog Radio Access Network (F-RAN) system equipped with a multicast fronthaul link. In this system, a control unit (CU) in the baseband processing unit (BBU) pool is connected to distributed edge nodes (ENs) through a multicast fronthaul link of finite capacity, and the ENs have baseband processing and caching capabilities. Each user equipment (UE) requests a file in a content library which is available at the CU, and the requested files are served by the closest ENs based on the cached contents and on the information received on the multicast fronthaul link. The performance of coded multicast fronthauling is investigated in terms of the delivery latency of the requested contents under the assumption of pipelined transmission on the fronthaul and edge links and of single-user encoding and decoding strategies based on the hard transfer of files on the fronthaul links. Extensive numerical results are provided to validate the advantages of the coded multicasting scheme compared to uncoded unicast and multicast strategies.
Fog Radio Access Network (F-RAN) exploits cached contents at edge nodes (ENs) and fronthaul connection to the cloud for content delivery. Assuming dedicated fronthaul links between cloud and each EN, previous works focused on analyses of F-RANs using offline or online caching depending whether the content popularity is time-invariant or time-variant. Extension has been done for multicast fronthaul link connecting cloud to only two ENs and time-invariant popularity. In contrast, the scope of this work is on the case where multicast fronthaul link connects arbitrary number of ENs to the cloud and content popularity is time-variant. Normalized Delivery Time (NDT) is used as a performance measure and by investigating proactive online caching, analytical results reveal that the power scaling of fronthaul transmission sets a limit on the performance of F-RAN.
In this paper, we study the resource allocation problem for a cooperative device-to-device (D2D)-enabled wireless caching network, where each user randomly caches popular contents to its memory and shares the contents with nearby users through D2D links. To enhance the throughput of spectrum sharing D2D links, which may be severely limited by the interference among D2D links, we enable the cooperation among some of the D2D links to eliminate the interference among them. We formulate a joint link scheduling and power allocation problem to maximize the overall throughput of cooperative D2D links (CDLs) and non-cooperative D2D links (NDLs), which is NP-hard. To solve the problem, we decompose it into two subproblems that maximize the sum rates of the CDLs and the NDLs, respectively. For CDL optimization, we propose a semi-orthogonal-based algorithm for joint user scheduling and power allocation. For NDL optimization, we propose a novel low-complexity algorithm to perform link scheduling and develop a Difference of Convex functions (D.C.) programming method to solve the non-convex power allocation problem. Simulation results show that the cooperative transmission can significantly increase both the number of served users and the overall system throughput.
A Fog-Radio Access Network (F-RAN) is studied in which cache-enabled Edge Nodes (ENs) with dedicated fronthaul connections to the cloud aim at delivering contents to mobile users. Using an information-theoretic approach, this work tackles the problem of quantifying the potential latency reduction that can be obtained by enabling Device-to-Device (D2D) communication over out-of-band broadcast links. Following prior work, the Normalized Delivery Time (NDT) --- a metric that captures the high signal-to-noise ratio worst-case latency --- is adopted as the performance criterion of interest. Joint edge caching, downlink transmission, and D2D communication policies based on compress-and-forward are proposed that are shown to be information-theoretically optimal to within a constant multiplicative factor of two for all values of the problem parameters, and to achieve the minimum NDT for a number of special cases. The analysis provides insights on the role of D2D cooperation in improving the delivery latency.
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