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Journal articles on the topic 'Wireless caching'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Zhou, Mo, Bo Ji, Kun Peng Han, and Hong Sheng Xi. "A Cooperative Hybrid Caching Strategy for P2P Mobile Network." Applied Mechanics and Materials 347-350 (August 2013): 1992–96. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1992.

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Recently mobile network technologies develop quickly. To meet the increasing demand of wireless users, many multimedia proxies have been deployed over wireless networks. The caching nodes constitute a wireless caching system with an architecture of P2P and provide better service to mobile users. In this paper, we formulate the caching system to optimize the consumption of network bandwidth and guarantee the response time of mobile users. Two strategies: single greedy caching strategy and cooperative hybrid caching strategy are proposed to achieve this goal. Single greedy caching aims to reduce bandwidth consumption from the standpoint of each caching node, while cooperative hybrid caching allows sharing and coordination of multiple nodes, taking both bandwidth consumption and popularity into account. Simulation results show that cooperative hybrid caching outperforms single greedy caching in both bandwidth consumption and delay time.

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2

Niesen, Urs, Devavrat Shah, and Gregory W. Wornell. "Caching in Wireless Networks." IEEE Transactions on Information Theory 58, no. 10 (October 2012): 6524–40. http://dx.doi.org/10.1109/tit.2012.2205733.

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3

Wang, James Z., Zhidian Du, and Pradip K. Srimani. "Cooperative Proxy Caching for Wireless Base Stations." Mobile Information Systems 3, no. 1 (2007): 1–18. http://dx.doi.org/10.1155/2007/371572.

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This paper proposes a mobile cache model to facilitate the cooperative proxy caching in wireless base stations. This mobile cache model uses a network cache line to record the caching state information about a web document for effective data search and cache space management. Based on the proposed mobile cache model, a P2P cooperative proxy caching scheme is proposed to use a self-configured and self-managed virtual proxy graph (VPG), independent of the underlying wireless network structure and adaptive to the network and geographic environment changes, to achieve efficient data search, data cache and date replication. Based on demand, the aggregate effect of data caching, searching and replicating actions by individual proxy servers automatically migrates the cached web documents closer to the interested clients. In addition, a cache line migration (CLM) strategy is proposed to flow and replicate the heads of network cache lines of web documents associated with a moving mobile host to the new base station during the mobile host handoff. These replicated cache line heads provide direct links to the cached web documents accessed by the moving mobile hosts in the previous base station, thus improving the mobile web caching performance. Performance studies have shown that the proposed P2P cooperative proxy caching schemes significantly outperform existing caching schemes.

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4

Li, Feng, Kwok-Yan Lam, Li Wang, Zhenyu Na, Xin Liu, and Qing Pan. "Caching Efficiency Enhancement at Wireless Edges with Concerns on User’s Quality of Experience." Wireless Communications and Mobile Computing 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1680641.

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Content caching is a promising approach to enhancing bandwidth utilization and minimizing delivery delay for new-generation Internet applications. The design of content caching is based on the principles that popular contents are cached at appropriate network edges in order to reduce transmission delay and avoid backhaul bottleneck. In this paper, we propose a cooperative caching replacement and efficiency optimization scheme for IP-based wireless networks. Wireless edges are designed to establish a one-hop scope of caching information table for caching replacement in cases when there is not enough cache resource available within its own space. During the course, after receiving the caching request, every caching node should determine the weight of the required contents and provide a response according to the availability of its own caching space. Furthermore, to increase the caching efficiency from a practical perspective, we introduce the concept of quality of user experience (QoE) and try to properly allocate the cache resource of the whole networks to better satisfy user demands. Different caching allocation strategies are devised to be adopted to enhance user QoE in various circumstances. Numerical results are further provided to justify the performance improvement of our proposal from various aspects.

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Zhao, Nan, Jun Li, Tao Han, Zheng Chang, and Lisheng Fan. "Wireless Caching Aided 5G Networks." Wireless Communications and Mobile Computing 2018 (June 6, 2018): 1. http://dx.doi.org/10.1155/2018/8764289.

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6

Chae, Seong Ho, and Wan Choi. "Caching Placement in Stochastic Wireless Caching Helper Networks: Channel Selection Diversity via Caching." IEEE Transactions on Wireless Communications 15, no. 10 (October 2016): 6626–37. http://dx.doi.org/10.1109/twc.2016.2586841.

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7

Hao, Yixue, Min Chen, Donggang Cao, Wenlai Zhao, Ivan Petrov, Vitaly Antonenko, and Ruslan Smeliansky. "Cognitive-Caching: Cognitive Wireless Mobile Caching by Learning Fine-Grained Caching-Aware Indicators." IEEE Wireless Communications 27, no. 1 (February 2020): 100–106. http://dx.doi.org/10.1109/mwc.001.1900273.

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8

Li, Yi, Chen Zhong, M. Cenk Gursoy, and Senem Velipasalar. "Learning-Based Delay-Aware Caching in Wireless D2D Caching Networks." IEEE Access 6 (2018): 77250–64. http://dx.doi.org/10.1109/access.2018.2881038.

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9

Chen, Wei, and H. Vincent Poor. "Wireless Caching: Making Radio Access Networks More than Bit-Pipelines." Network 1, no. 2 (August 16, 2021): 146–64. http://dx.doi.org/10.3390/network1020010.

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Caching has attracted much attention recently because it holds the promise of scaling the service capability of radio access networks (RANs). We envision that caching will ultimately make next-generation RANs more than bit-pipelines and emerge as a multi-disciplinary area via the union with communications, pricing, recommendation, compression, and computation units. By summarizing cutting-edge caching policies, we trace a common root of their gains to the prolonged transmission time, which is then traded for higher spectral or energy efficiency. To realize caching, the physical layer and higher layers have to function together, with the aid of prediction and memory units, which substantially broadens the concept of cross-layer design to a multi-unit collaboration methodology. We revisit caching from a generalized cross-layer perspective, with a focus on its emerging opportunities, challenges, and theoretical performance limits. To motivate the application and evolution of caching, we conceive a hierarchical pricing infrastructure that provides incentives to network operators and users. To make RANs even more proactive, we design caching and recommendation jointly, showing a user what it might be interested in and what has been done for it. Furthermore, the user-specific demand prediction motivates edge compression and proactive MEC as new applications. The beyond-bit-pipeline RAN is a paradigm shift that brings with it many cross-disciplinary research opportunities.

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10

Poularakis, Konstantinos, and Leandros Tassiulas. "Cooperation and information replication in wireless networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2062 (March 6, 2016): 20150123. http://dx.doi.org/10.1098/rsta.2015.0123.

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A significant portion of today's network traffic is due to recurring downloads of a few popular contents. It has been observed that replicating the latter in caches installed at network edges—close to users—can drastically reduce network bandwidth usage and improve content access delay. Such caching architectures are gaining increasing interest in recent years as a way of dealing with the explosive traffic growth, fuelled further by the downward slope in storage space price. In this work, we provide an overview of caching with a particular emphasis on emerging network architectures that enable caching at the radio access network. In this context, novel challenges arise due to the broadcast nature of the wireless medium, which allows simultaneously serving multiple users tuned into a multicast stream, and the mobility of the users who may be frequently handed off from one cell tower to another. Existing results indicate that caching at the wireless edge has a great potential in removing bottlenecks on the wired backbone networks. Taking into consideration the schedule of multicast service and mobility profiles is crucial to extract maximum benefit in network performance.

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11

Shi, Fang, Lisheng Fan, Xin Liu, Zhenyu Na, and Yanchen Liu. "Probabilistic Caching Placement in the Presence of Multiple Eavesdroppers." Wireless Communications and Mobile Computing 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/2104162.

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The wireless caching has attracted a lot of attention in recent years, since it can reduce the backhaul cost significantly and improve the user-perceived experience. The existing works on the wireless caching and transmission mainly focus on the communication scenarios without eavesdroppers. When the eavesdroppers appear, it is of vital importance to investigate the physical-layer security for the wireless caching aided networks. In this paper, a caching network is studied in the presence of multiple eavesdroppers, which can overhear the secure information transmission. We model the locations of eavesdroppers by a homogeneous Poisson Point Process (PPP), and the eavesdroppers jointly receive and decode contents through the maximum ratio combining (MRC) reception which yields the worst case of wiretap. Moreover, the main performance metric is measured by the average probability of successful transmission, which is the probability of finding and successfully transmitting all the requested files within a radius R. We study the system secure transmission performance by deriving a single integral result, which is significantly affected by the probability of caching each file. Therefore, we extend to build the optimization problem of the probability of caching each file, in order to optimize the system secure transmission performance. This optimization problem is nonconvex, and we turn to use the genetic algorithm (GA) to solve the problem. Finally, simulation and numerical results are provided to validate the proposed studies.

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12

Li, Wenkai, Chenyang Wang, Ding Li, Bin Hu, Xiaofei Wang, and Jianji Ren. "Edge Caching for D2D Enabled Hierarchical Wireless Networks with Deep Reinforcement Learning." Wireless Communications and Mobile Computing 2019 (February 27, 2019): 1–12. http://dx.doi.org/10.1155/2019/2561069.

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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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13

Li, Qi, Xiaoxiang Wang, Dongyu Wang, Yibo Zhang, Yanwen Lan, Qiang Liu, and Lei Song. "Analysis of an SDN-Based Cooperative Caching Network with Heterogeneous Contents." Electronics 8, no. 12 (December 6, 2019): 1491. http://dx.doi.org/10.3390/electronics8121491.

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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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14

Kwak, Jeongho, Yeongjin Kim, Long Bao Le, and Song Chong. "Hybrid Content Caching in 5G Wireless Networks: Cloud Versus Edge Caching." IEEE Transactions on Wireless Communications 17, no. 5 (May 2018): 3030–45. http://dx.doi.org/10.1109/twc.2018.2805893.

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15

Molisch, Andreas F., Giuseppe Caire, David Ott, Jeffrey R. Foerster, Dilip Bethanabhotla, and Mingyue Ji. "Caching Eliminates the Wireless Bottleneck in Video Aware Wireless Networks." Advances in Electrical Engineering 2014 (November 30, 2014): 1–13. http://dx.doi.org/10.1155/2014/261390.

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Wireless video is the main driver for rapid growth in cellular data traffic. Traditional methods for network capacity increase are very costly and do not exploit the unique features of video, especially asynchronous content reuse. In this paper we give an overview of our work that proposed and detailed a new transmission paradigm exploiting content reuse and the widespread availability of low-cost storage. Our network structure uses caching in helper stations (femtocaching) and/or devices, combined with highly spectrally efficient short-range communications to deliver video files. For femtocaching, we develop optimum storage schemes and dynamic streaming policies that optimize video quality. For caching on devices, combined with device-to-device (D2D) communications, we show that communications within clusters of mobile stations should be used; the cluster size can be adjusted to optimize the tradeoff between frequency reuse and the probability that a device finds a desired file cached by another device in the same cluster. In many situations the network throughput increases linearly with the number of users, and the tradeoff between throughput and outage is better than in traditional base-station centric systems. Simulation results with realistic numbers of users and channel conditions show that network throughput can be increased by two orders of magnitude compared to conventional schemes.

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16

Bani-Bakr, Alaa, MHD Nour Hindia, Kaharudin Dimyati, Effariza Hanafi, and Tengku Faiz Tengku Mohmed Noor Izam. "Multi-Objective Caching Optimization for Wireless Backhauled Fog Radio Access Network." Symmetry 13, no. 4 (April 17, 2021): 708. http://dx.doi.org/10.3390/sym13040708.

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Proactive content caching in a fog radio access network (F-RAN) is an efficient technique used to alleviate delivery delay and traffic congestion. However, the symmetric caching of the content is impractical due to the dissimilarity among the contents popularity. Therefore, in this paper, a multi-objective random caching scheme to balance the successful transmission probability (STP) and delay in wireless backhauled F-RAN is proposed. First, stochastic geometry tools are utilized to derive expressions of the association probability, STP, and average delivery delay. Next, the complexity is reduced by considering the asymptotic STP and delay in the high signal-to-noise ratio (SNR) regime. Then, aiming at maximizing the STP or minimizing the delay, the multi-objective cache placement optimization problem is formulated. A novel projected multi-objective cuckoo search algorithm (PMOCSA) is proposed to obtain the Pareto front of the optimal cache placement. The numerical results show that PMOCSA outperforms the original multi-objective cuckoo search algorithm (MOCSA) in terms of convergence to a feasible Pareto front and its rate. It also shows that the proposed multi-objective caching scheme significantly outperforms the well-known benchmark caching schemes by up to 40% higher STP and 85% lower average delay.

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17

Lee, Ming-Chun, Hao Feng, and Andreas F. Molisch. "Dynamic Caching Content Replacement in Base Station Assisted Wireless D2D Caching Networks." IEEE Access 8 (2020): 33909–25. http://dx.doi.org/10.1109/access.2020.2973953.

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18

TAKITA, Makoto, Masanori HIROTOMO, and Masakatu MORII. "Coded Caching in Multi-Rate Wireless Networks." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E103.A, no. 12 (December 1, 2020): 1347–55. http://dx.doi.org/10.1587/transfun.2020tap0013.

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19

Karmoose, Mohammed, Martina Cardone, and Christina Fragouli. "Simplifying Wireless Social Caching via Network Coding." IEEE Transactions on Communications 66, no. 11 (November 2018): 5512–25. http://dx.doi.org/10.1109/tcomm.2018.2854786.

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20

Shariatpanahi, Seyed Pooya, Babak Hossein Khalaj, and Hamed Shah-Mansouri. "Caching gain in interference-limited wireless networks." IET Communications 9, no. 10 (July 2, 2015): 1269–77. http://dx.doi.org/10.1049/iet-com.2014.0955.

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21

Kumar, Harish, and Mritunjay Kumar Rai. "Caching in Wireless Sensor Networks: A Survey." International Journal of Engineering Trends and Technology 10, no. 11 (April 25, 2014): 550–53. http://dx.doi.org/10.14445/22315381/ijett-v10p309.

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22

Jeon, Sang-Woon, Song-Nam Hong, Mingyue Ji, Giuseppe Caire, and Andreas F. Molisch. "Wireless Multihop Device-to-Device Caching Networks." IEEE Transactions on Information Theory 63, no. 3 (March 2017): 1662–76. http://dx.doi.org/10.1109/tit.2017.2654341.

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23

Paschos, Georgios, Ejder Bastug, Ingmar Land, Giuseppe Caire, and Merouane Debbah. "Wireless caching: technical misconceptions and business barriers." IEEE Communications Magazine 54, no. 8 (August 2016): 16–22. http://dx.doi.org/10.1109/mcom.2016.7537172.

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24

Hu, Zhiwen, Zijie Zheng, Tao Wang, Lingyang Song, and Xiaoming Li. "Game theoretic approaches for wireless proactive caching." IEEE Communications Magazine 54, no. 8 (August 2016): 37–43. http://dx.doi.org/10.1109/mcom.2016.7537175.

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25

Srinivas Goud, Surgi, and Shankar Thalla. "Distributed Cooperative Caching in Social Wireless Networks." International Journal of Computer Trends and Technology 14, no. 3 (August 25, 2014): 105–9. http://dx.doi.org/10.14445/22312803/ijctt-v14p123.

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26

Sharma, T. P., R. C. Joshi, and Manoj Misra. "Cooperative caching for homogeneous wireless sensor networks." International Journal of Communication Networks and Distributed Systems 2, no. 4 (2009): 424. http://dx.doi.org/10.1504/ijcnds.2009.026557.

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27

Taghizadeh, Mahmoud, Kristopher Micinski, Subir Biswas, Charles Ofria, and Eric Torng. "Distributed Cooperative Caching in Social Wireless Networks." IEEE Transactions on Mobile Computing 12, no. 6 (June 2013): 1037–53. http://dx.doi.org/10.1109/tmc.2012.66.

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28

Katsaros, D., and Y. Manolopoulos. "Web caching in broadcast mobile wireless environments." IEEE Internet Computing 8, no. 3 (May 2004): 37–45. http://dx.doi.org/10.1109/mic.2004.1297272.

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29

Shariatpanahi, Seyed Pooya, Giuseppe Caire, and Babak Hossein Khalaj. "Physical-Layer Schemes for Wireless Coded Caching." IEEE Transactions on Information Theory 65, no. 5 (May 2019): 2792–807. http://dx.doi.org/10.1109/tit.2018.2888615.

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30

Chauhan, Naveen, Lalit K. Awasthi, Narottam Chand, R. C. Joshi, and Manoj Misra. "Cooperative Caching in Mobile Ad Hoc Networks." International Journal of Mobile Computing and Multimedia Communications 3, no. 3 (July 2011): 20–35. http://dx.doi.org/10.4018/jmcmc.2011070102.

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Mobile ad hoc network (MANET) presents a constrained communication environment due to fundamental limitations of client’s resources, insufficient wireless bandwidth and users’ frequent mobility. MANETs have many distinct characteristics which distinguish them from other wireless networks. Due to frequent network disconnection, data availability is lower than traditional wired networks. Cooperative caching helps MANETs in alleviating the situation of non availability of data. In this paper, the authors present a scheme called global cluster cooperation (GCC) for caching in mobile ad hoc networks. In this scheme, network topology is partitioned into non-overlapping clusters based on the physical network proximity. This approach fully exploits the pull mechanism to facilitate cache sharing in a MANET. Simulation experiments show that GCC mechanism achieves significant improvements in cache hit ratio and average query latency in comparison with other caching strategies.

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31

Li, Lincan, Chiew Foong Kwong, Qianyu Liu, Pushpendu Kar, and Saeid Pourroostaei Ardakani. "A Novel Cooperative Cache Policy for Wireless Networks." Wireless Communications and Mobile Computing 2021 (August 6, 2021): 1–18. http://dx.doi.org/10.1155/2021/5568935.

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Mobile edge caching is an emerging approach to manage high mobile data traffic in fifth-generation wireless networks that reduces content access latency and offloading data traffic of backhaul links. This paper proposes a novel cooperative caching policy based on long short-term memory (LSTM) neural networks considering the characteristics between the features of the heterogeneous layers and the user moving speed. Specifically, LSTM is applied to predict content popularity. Size-weighted content popularity is utilised to balance the impact of the predicted content popularity and content size. We also consider the moving speeds of mobile users and introduce a two-level caching architecture consisting of several small base stations (SBSs) and macro base stations (MBSs). To avoid content requests of fast-moving users affecting the content popularity distribution of the SBS since fast-moving users frequently handover among SBSs, fast-moving users are served by MBSs no matter which SBS they are in. SBSs serve low-speed users, and SBSs in the same cluster can communicate with one another. The simulation results show that compared to common cache methods, for example, the least frequently used and least recently used methods, our proposed policy is at least 8.9% lower and 6.8% higher in terms of the average content access latency and offloading ratio, respectively.

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32

Liang, Chengchao, Ying He, F. Richard Yu, and Nan Zhao. "Enhancing QoE-Aware Wireless Edge Caching With Software-Defined Wireless Networks." IEEE Transactions on Wireless Communications 16, no. 10 (October 2017): 6912–25. http://dx.doi.org/10.1109/twc.2017.2734081.

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Shan, Chun, Xiao-ping Wu, Yan Liu, Jun Cai, and Jian-zhen Luo. "IBP Based Caching Strategy in D2D." Applied Sciences 9, no. 12 (June 13, 2019): 2416. http://dx.doi.org/10.3390/app9122416.

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Device to Device (D2D) communication is a key technology in 5th generation wireless systems to increase communication capacity and spectral efficiency. Applying caching into D2D communication networks, the device can retrieve content from other devices by establishing D2D communication links. In this way, the backhaul traffic can be significantly reduced. However, most of the existing caching schemes in D2D are proactive caching, which cannot satisfy the requirement of real-time updating. In this paper, we propose an Indian Buffet Process based D2D caching strategy (IBPSC). Firstly, we construct a geographical D2D communication network to provide high quality D2D communications according to physical closeness between devices. Then devices are divided into several social communities. Devices are ranked by their node importance to community in each community. The base station makes caching decisions for devices according to contrition degree. Experimental results show that IBPSC achieves best network performance.

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THRA, T. SUJI, PHANI KUMAR NAGARAM, and MANASA NAGARAM. "Information Caching and Prefetching Using Collaborative Caching and Prefetching Algorithm in Wireless ADHOC Networks." International Journal of Innovative Research in Science, Engineering and Technology 7, no. 2 (February 15, 2018): 1589–91. http://dx.doi.org/10.15680/ijirset.2018.0702109.

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Rim, Minjoong, and Chung G. Kang. "Peak-Hour Caching Schemes of Mobile Devices for Overload Cells in Wireless Caching Systems." IEEE Access 8 (2020): 195274–89. http://dx.doi.org/10.1109/access.2020.3033619.

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Sung, Jihoon, June-Koo Kevin Rhee, and Sangsu Jung. "Lightweight caching strategy for wireless content delivery networks." IEICE Communications Express 3, no. 4 (2014): 150–55. http://dx.doi.org/10.1587/comex.3.150.

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Ding, Zhiguo, Pingzhi Fan, George K. Karagiannidis, Robert Schober, and H. Vincent Poor. "NOMA Assisted Wireless Caching: Strategies and Performance Analysis." IEEE Transactions on Communications 66, no. 10 (October 2018): 4854–76. http://dx.doi.org/10.1109/tcomm.2018.2841929.

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Kaur, Sajpreet, and Sukhwinder Singh. "Modified Circular Layer Caching in Wireless Sensor Networks." International Journal of Sensor and Its Applications for Control Systems 3, no. 1 (May 31, 2015): 7–14. http://dx.doi.org/10.14257/ijsacs.2015.3.1.02.

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Shanmugam, Karthikeyan, Negin Golrezaei, Alexandros G. Dimakis, Andreas F. Molisch, and Giuseppe Caire. "FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers." IEEE Transactions on Information Theory 59, no. 12 (December 2013): 8402–13. http://dx.doi.org/10.1109/tit.2013.2281606.

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Ji, Mingyue, Giuseppe Caire, and Andreas F. Molisch. "Fundamental Limits of Caching in Wireless D2D Networks." IEEE Transactions on Information Theory 62, no. 2 (February 2016): 849–69. http://dx.doi.org/10.1109/tit.2015.2504556.

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41

Tamoor-ul-Hassan, Syed, Sumudu Samarakoon, Mehdi Bennis, Matti Latva-aho, and Choong Seon Hong. "Learning-Based Caching in Cloud-Aided Wireless Networks." IEEE Communications Letters 22, no. 1 (January 2018): 137–40. http://dx.doi.org/10.1109/lcomm.2017.2759270.

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42

Zhao, Zhongyuan, Mingfeng Xu, Weiliang Xie, Zhiguo Ding, and George K. Karagiannidis. "Coverage Performance of NOMA in Wireless Caching Networks." IEEE Communications Letters 22, no. 7 (July 2018): 1458–61. http://dx.doi.org/10.1109/lcomm.2018.2830376.

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43

Poularakis, Konstantinos, George Iosifidis, Vasilis Sourlas, and Leandros Tassiulas. "Exploiting Caching and Multicast for 5G Wireless Networks." IEEE Transactions on Wireless Communications 15, no. 4 (April 2016): 2995–3007. http://dx.doi.org/10.1109/twc.2016.2514418.

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Song, Jaeyoung, Hojin Song, and Wan Choi. "Optimal Content Placement for Wireless Femto-Caching Network." IEEE Transactions on Wireless Communications 16, no. 7 (July 2017): 4433–44. http://dx.doi.org/10.1109/twc.2017.2698447.

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Vu, Thang X., Symeon Chatzinotas, and Bjorn Ottersten. "Edge-Caching Wireless Networks: Performance Analysis and Optimization." IEEE Transactions on Wireless Communications 17, no. 4 (April 2018): 2827–39. http://dx.doi.org/10.1109/twc.2018.2803816.

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46

Zhang, Zhilong, Jianmei Dai, Minyin Zeng, Danpu Liu, and Shiwen Mao. "Scalable Video Caching for Information Centric Wireless Networks." IEEE Access 8 (2020): 77272–84. http://dx.doi.org/10.1109/access.2020.2989532.

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47

Umrao, Sachin, Abhishek Roy, and Navrati Saxena. "Multilevel Hierarchical Caching for Efficient Wireless Video Distribution." IETE Journal of Research 63, no. 2 (November 24, 2016): 260–67. http://dx.doi.org/10.1080/03772063.2016.1249965.

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Amer, Ramy, M. Majid Butt, Mehdi Bennis, and Nicola Marchetti. "Inter-Cluster Cooperation for Wireless D2D Caching Networks." IEEE Transactions on Wireless Communications 17, no. 9 (September 2018): 6108–21. http://dx.doi.org/10.1109/twc.2018.2854603.

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Ming, Zhongxing, Mingwei Xu, and Dan Wang. "In-network caching assisted wireless AP storage management." ACM SIGCOMM Computer Communication Review 43, no. 4 (September 19, 2013): 521–22. http://dx.doi.org/10.1145/2534169.2491706.

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Mohajer, Soheil, Itsik Bergel, and Giuseppe Caire. "Cooperative Wireless Mobile Caching: A Signal Processing Perspective." IEEE Signal Processing Magazine 37, no. 2 (March 2020): 18–38. http://dx.doi.org/10.1109/msp.2019.2962507.

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