Cooperative Transmission via Caching Helpers

Chae, Seong Ho; Ryu, Jong Yeol; Quek, Tony Q. S.; Choi, Wan

doi:10.1109/glocom.2014.7417164

Cited by 32 publications

(39 citation statements)

References 17 publications

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“…The role of wireless content caching in the oncoming 5G network is demonstrated in [3,4], and its architecture is presented in [5] where the mobile edge applications can be conveniently provided by chaining the service functions with the aid of mobile edge computing and virtualized resources. As academic attention on caching technology has continuously increased, interesting studies have appeared on diverse caching networks and the corresponding caching strategies such as the femto-cellular network [6][7][8][9][10][11][12], device-to-device (D2D) network [2,[13][14][15][16][17], and heterogeneous network [18]. We discuss these works in additional detail.…”

Section: Related Workmentioning

confidence: 99%

“…The optimal cache distribution is obtained through optimization of the file transmission success probability [8]. In [9], in which the contents are cached into small-cell BSs, the authors designed distributed caching optimization algorithms via belief propagation to minimize the downloading latency with the aid of a factor graph. Cluster-centric small cell networks and a combined caching scheme were proposed [11] in which a protion of the cache space in each small-BS (SBS) cluster was reserved for storing the most popular contents in every SBS, and the remaining space was used to cooperatively cache different partitions of the less popular contents in different SBSs.…”

Section: Related Workmentioning

confidence: 99%

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Analysis of an SDN-Based Cooperative Caching Network with Heterogeneous Contents

Wang

et al. 2019

Electronics

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The ubiquity of data-enabled mobile devices and wireless-enabled data applications has fostered the rapid development of wireless content caching, which is an efficient approach to mitigating cellular traffic pressure. Considering the content characteristics and real caching circumstances, a software-defined network (SDN)-based cooperative caching system is presented. First, we define a new file block library with heterogeneous content attributes [file popularity, mobile user (MU) preference, file size]. An SDN-based three-tier caching network is presented in which the base station supplies control coverage for the entire macrocell and cache helpers (CHs), MUs with cache capacities offer data coverage. Using the ‘most popular content’ and ‘largest diversity content’, a distributed cooperative caching strategy is proposed in which the caches of the MUs store the most popular contents of the file block library to mitigate the effect of MU mobility, and those of the CHs store the remaining contents in a probabilistic caching manner to enrich the content diversity and reduce the MU caching pressure. The request meet probability (RMPro) is subsequently proposed, and the optimal caching distribution of the contents in the probabilistic caching strategy is obtained via optimization. Finally, using the result of RMPro optimization, we also analyze the content retrieval delays that occur when a typical MU requests a file block or a whole file. Simulation results demonstrate that the proposed caching system can achieve quasi-optimal revenue performance compared with other contrasting schemes.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Analysis of an SDN-Based Cooperative Caching Network with Heterogeneous Contents

Wang

et al. 2019

Electronics

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“…Femto caching proposed in [11,12] and AMVS-NDN proposed by [13] are both committed to adding the cache in BS for the purpose of unloading the traffic. The authors of [14][15][16] proposed a collaborative caching strategy between BS, which greatly improves the QoS of users. In recent years, the application of intelligence in wireless networks is getting more and more attention.…”

Section: Related Workmentioning

confidence: 99%

Edge Caching for D2D Enabled Hierarchical Wireless Networks with Deep Reinforcement Learning

Wang

Ding

et al. 2019

Wireless Communications and Mobile Computing

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Edge caching is a promising method to deal with the traffic explosion problem towards future network. In order to satisfy the demands of user requests, the contents can be proactively cached locally at the proximity to users (e.g., base stations or user device). Recently, some learning-based edge caching optimizations are discussed. However, most of the previous studies explore the influence of dynamic and constant expanding action and caching space, leading to unpracticality and low efficiency. In this paper, we study the edge caching optimization problem by utilizing the Double Deep Q-network (Double DQN) learning framework to maximize the hit rate of user requests. Firstly, we obtain the Device-to-Device (D2D) sharing model by considering both online and offline factors and then we formulate the optimization problem, which is proved as NP-hard. Then the edge caching replacement problem is derived by Markov decision process (MDP). Finally, an edge caching strategy based on Double DQN is proposed. The experimental results based on large-scale actual traces show the effectiveness of the proposed framework.

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“…The average probability of successful content delivery was derived in [20], where it was necessary to quantify the success of content delivery during caching placement. In [23], the average probability of successful file transmission was maximized to determine the success of file transmission in a wireless cache-enabled system of small cells. The authors in [24] described a probabilistic cache placement scheme that takes into account the cache-aided throughput; the scheme measures the density of successfully served requests through the local cache of the requesting user or through nearby devices via D2D transmission.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks

Soleimani

Tao

2018

Applied Sciences

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In cache-enabled device-to-device (D2D) -aided cellular networks, the technique of caching contents in the cooperative crossing between base stations (BSs) and devices can significantly reduce core traffic and enhance network capacity. In this paper, we propose a scheme that establishes device availability, which indicates whether a cache-enabled device can handle the transmission of the desired content within the required sending time, called the delay, while achieving optimal probabilistic caching. We also investigate the impact of transmission device availability on the effectiveness of a scenario of cooperative crossing cache placement, where content delivery traffic can be offloaded from the local cache, a D2D transmitter’s cache via a D2D link, or else directly from a BS via a cellular link, in order to maximize the offloading probability. Further, we derive the cooperation content offloading strategy while considering successful content transmission by D2D transmitters or BSs to guarantee the delay, even though reducing the delay is not the focus of this study. Finally, the proposed problem is formulated. Owing to the non-convexity of the optimization problem, it can be rewritten as a minimization of the difference between the convex functions; thus, it can be solved by difference of convex (DC) programming using a low-complexity algorithm. Simulation results show that the proposed cache placement scheme improves the offloading probability by 13.5% and 23% compared to Most Popular Content (MPC) scheme, in which both BSs and devices cache the most popular content and Coop. BS/D2D caching scheme, in which each BS tier and user tier applies cooperative content caching separately.

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Cooperative Transmission via Caching Helpers

Cited by 32 publications

References 17 publications

Analysis of an SDN-Based Cooperative Caching Network with Heterogeneous Contents

Analysis of an SDN-Based Cooperative Caching Network with Heterogeneous Contents

Edge Caching for D2D Enabled Hierarchical Wireless Networks with Deep Reinforcement Learning

Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks

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