Wireless caching: technical misconceptions and business barriers

Paschos, Georgios S.; Baştuğ, Ejder; Land, Ingmar; Caire, Giuseppe; Debbah, Mérouane

doi:10.1109/mcom.2016.7537172

Cited by 312 publications

(286 citation statements)

References 11 publications

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“…Deploying cache-enabled small base stations (SBSs) in ultra-dense heterogeneous networks (HetNets) has been considered as one of the promising solutions to meet the high performance requirements in the fifth generation (5G) systems [1]. Caching the files with high popularity to SBSs and directly serving most of the users' content requests from the SBSs' local caches, are beneficial towards reducing latency and network traffic load.…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for optimizing content delivering in cache-enabled HetNet

Lei

You

Dai

et al. 2017

2017 International Symposium on Wireless Communication Systems (ISWCS)

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Abstract-In ultra-dense heterogeneous networks, caching popular contents at small base stations is considered as an effective way to reduce latency and redundant data transmission. Optimization of caching placement/replacement and content delivering can be computationally heavy, especially for largescale networks. The provision of both time-efficient and highquality solutions is challenging. Conventional iterative optimization methods, either optimal or heuristic, typically require a large number of iterations to achieve satisfactory performance, and result in considerable computational delay. This may limit their applications in practical network operations where online decisions have to be made. In this paper, we provide a viable alternative to the conventional methods for caching optimization, from a deep learning perspective. The idea is to train the optimization algorithms through a deep neural network (DNN) in advance, instead of directly applying them in real-time caching or scheduling. This allows significant complexity reduction in the delay-sensitive operation phase since the computational burden is shifted to the DDN training phase. Numerical results demonstrate that the DNN is of high computational efficiency. By training the designed DNN over a massive number of instances, the solution quality of the energy-efficient content delivering can be progressively approximated to around 90% of the optimum.

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Section: Introductionmentioning

confidence: 99%

A deep learning approach for optimizing content delivering in cache-enabled HetNet

Lei

You

Dai

et al. 2017

2017 International Symposium on Wireless Communication Systems (ISWCS)

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“…Nowadays, with the rapid development of mobile communication technologies and mobile devices, wireless data traffic is experiencing an explosive increase, especially in terms of mobile video streaming, high definition (HD) video and video webcasting [1]. A recent Cisco report estimates that the global mobile data volume will grow nearly ten times in the next five years, and the world's mobile data traffic will reach 30.6 monthly exabytes by 2020 [2].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Caching for Mobile Edge Computing in 5G Networks

et al. 2017

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Abstract:Mobile Edge Computing (MEC), which is considered a promising and emerging paradigm to provide caching capabilities in proximity to mobile devices in 5G networks, enables fast, popular content delivery of delay-sensitive applications at the backhaul capacity of limited mobile networks. Most existing studies focus on cache allocation, mechanism design and coding design for caching. However, grid power supply with fixed power uninterruptedly in support of a MEC server (MECS) is costly and even infeasible, especially when the load changes dynamically over time. In this paper, we investigate the energy consumption of the MECS problem in cellular networks. Given the average download latency constraints, we take the MECS's energy consumption, backhaul capacities and content popularity distributions into account and formulate a joint optimization framework to minimize the energy consumption of the system. As a complicated joint optimization problem, we apply a genetic algorithm to solve it. Simulation results show that the proposed solution can effectively determine the near-optimal caching placement to obtain better performance in terms of energy efficiency gains compared with conventional caching placement strategies. In particular, it is shown that the proposed scheme can significantly reduce the joint cost when backhaul capacity is low.

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“…Recently, wireless caching has been proposed as a viable solution to enhance the capacity of small cell backhauling [5]- [15]. Built upon the content-centric networking paradigm, in wireless caching, the most popular contents are pre-stored at the access points or BSs in close proximity of the user equipments (UEs).…”

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confidence: 99%

“…Built upon the content-centric networking paradigm, in wireless caching, the most popular contents are pre-stored at the access points or BSs in close proximity of the user equipments (UEs). Consequently, the backhaul traffic is offloaded by reusing the cached content [5], [6]. Caching as an alternative to small cell backhauling was first investigated in [7], where caching was shown to also substantially reduce the average downloading delay.…”

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confidence: 99%

“…YouTube and Netflix, mainly rely on end-toend encryption schemes such as the hypertext transfer protocol secure (HTTPS) [23] to ensure communication security. However, with such schemes, the benefits of content-centric networking vanish as encrypted contents are uniquely defined for each user request and cannot be reused to serve other user requests [5]. For this reason, caching was mainly considered for content without security restrictions in the literature [5]- [10].…”

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confidence: 99%

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Secure Video Streaming in Heterogeneous Small Cell Networks With Untrusted Cache Helpers

Xiang

Schober

et al. 2018

IEEE Trans. Wireless Commun.

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Abstract-This paper studies secure video streaming in cacheenabled small cell networks, where some of the cache-enabled small cell base stations (BSs) helping in video delivery are untrusted. Unfavorably, caching improves the eavesdropping capability of these untrusted helpers as they may intercept both the cached and the delivered video files. To address this issue, we propose joint caching and scalable video coding (SVC) of video files to enable secure cooperative multiple-input multipleoutput (MIMO) transmission and, at the same time, exploit the cache memory of both the trusted and untrusted BSs for improving the system performance. Considering imperfect channel state information (CSI) at the transmitters, we formulate a two-timescale non-convex mixed-integer robust optimization problem to minimize the total transmit power required for guaranteeing the quality of service (QoS) and secrecy during video streaming. We develop an iterative algorithm based on a modified generalized Benders decomposition (GBD) to solve the problem optimally, where the caching and the cooperative transmission policies are determined via offline (long-timescale) and online (short-timescale) optimization, respectively. Furthermore, inspired by the optimal algorithm, a low-complexity suboptimal algorithm based on a greedy heuristic is proposed. Simulation results show that the proposed schemes achieve significant gains in power efficiency and secrecy performance compared to several baseline schemes.

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Wireless caching: technical misconceptions and business barriers

Cited by 312 publications

References 11 publications

A deep learning approach for optimizing content delivering in cache-enabled HetNet

A deep learning approach for optimizing content delivering in cache-enabled HetNet

Energy-Efficient Caching for Mobile Edge Computing in 5G Networks

Secure Video Streaming in Heterogeneous Small Cell Networks With Untrusted Cache Helpers

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