Fundamental Limits of Caching

Maddah-Ali, Mohammad Ali; Niesen, Urs

doi:10.1109/tit.2014.2306938

Cited by 1,497 publications

(352 citation statements)

References 15 publications

Supporting

Mentioning

351

Contrasting

Order By: Relevance

“…As in [1], the measure of performance here is the duration T -in time slots, per file served per user -needed to complete the delivery process, for any request. Time is normalized such that one time slot corresponds to the amount of time it would take to communicate a single file to a single receiver, had their been no caching and no interference.…”

Section: B Measure Of Performancementioning

confidence: 99%

“…Time is normalized such that one time slot corresponds to the amount of time it would take to communicate a single file to a single receiver, had their been no caching and no interference. As a result, in the high P setting of interest, with the capacity of a MISO channel scaling as log P , we proceed to set f = log P , which guarantees that the two measures of performance, here and in [1], now carry the same meaning, and can be meaningfully compared 2 .…”

Section: B Measure Of Performancementioning

confidence: 99%

“…In the setting of communication networks, recent work in [1] has explored how caching content at receiving users, can increase the effective throughput and can reduce the load on the network, by utilizing multicast gains, i.e., by creating the need for common symbols that are simultaneously needed by more than one user. Our interest here is to explore this idea, not in the original multicast setting in [1], but in feedbackaided broadcast-type settings which utilize broadcast gains to communicate private messages.…”

Section: Introductionmentioning

confidence: 99%

“…Our interest here is to explore this idea, not in the original multicast setting in [1], but in feedbackaided broadcast-type settings which utilize broadcast gains to communicate private messages. Our goal is to explore how caching, and the associated common messages that are generated as a result of this caching, can result in CSIT reductions, while at the same time, exploring how to properly balance the multicast and broadcast elements that emerge.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Coded caching for reducing CSIT-feedback in wireless communications

Zhang

Engelmann

Elia

2015

2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)

View full text Add to dashboard Cite

The work explores the role of caching content at receiving users for the purpose of reducing the need for feedback in wireless communications. In the K-user broadcast channel (BC), we show how caching, when combined with a rate-splitting broadcast approach, can not only improve performance, but can also reduce the need for channel state information at the transmitter (CSIT), in the sense that the identified cache-aided optimal degrees-of-freedom performance, can in fact be achieved with reduced-quality CSIT. These CSIT savings can be traced back to an inherent relationship between caching, performance, and CSIT; caching improves performance by leveraging multicasting of common information, which automatically reduces the need for CSIT, by virtue of the fact that common information is not a cause of interference. At the same time though, too much multicasting of common information can be detrimental, as it does not utilize existing CSIT. Our caching method builds on the Maddah-Ali and Niesen coded caching scheme, by properly balancing multicast and broadcast opportunities, and by combing caching with rate-splitting communication schemes that are specifically designed to operate under imperfect-quality CSIT. The observed achievable CSIT savings here, are more pronounced for smaller values of K users and N files.

show abstract

Section: B Measure Of Performancementioning

confidence: 99%

Section: B Measure Of Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Coded caching for reducing CSIT-feedback in wireless communications

Zhang

Engelmann

Elia

2015

2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)

View full text Add to dashboard Cite

show abstract

“…The information theoretic formulation of this problem was introduced recently in [1] where an order-optimal coded caching scheme was also proposed. There has also been a lot of work on exploiting caching in small cell networks to alleviate backhaul load [2] and utilize content reuse through D2D communication [3].…”

Section: Introductionmentioning

confidence: 99%

Distributed caching in device-to-device networks: A stochastic geometry perspective

Krishnan

Dhillon

2015

2015 49th Asilomar Conference on Signals, Systems and Computers

View full text Add to dashboard Cite

Increasing spatio-temporal correlation in the data demand makes it attractive to cache popular content directly on the user devices so that it can be delivered on demand to the neighboring devices through device-to-device (D2D) communications. Due to the limited storage capacity, each device can only cache a part of the popular content resulting in a distributed caching network. With device locations modeled as a Poisson Point Process (PPP), we assume that the file of interest for a typical device is partitioned into several file portions with each device caching one of the portions randomly. Two equivalent viewpoints for the analysis of coverage probability and average delay in a distributed caching network are discussed for varying cache probabilities and device activity factors.

show abstract

Proactive Caching in 5G Small Cell Networks

2016

View full text Add to dashboard Cite

Massive deployment of small-cell base stations (SBSs) is going to play a key role for capacity and coverage enhancements in 5G networks. However, the backhaul for these networks remains one of the important issue to solve. Ideally, the capacity of backhaul has to be in the same order of wireless links in order to avoid bottlenecks in the delivery and sustain huge traffic generated by mobile users, especially due to video streaming and content sharing in social networks. In reality, the deployment of such high-speed backhauls is not straightforward due to its costly nature. Thus, one promising way of tackling this backhaul bottleneck and satisfying users' demand is to cache the strategic contents at the edge of the network, namely at the SBSs and user terminals (UTs). So far, most of the existing solutions are based on the reactive networking paradigm in which users' content requests are served immediately upon their arrival or causing outages otherwise. In this chapter, we first provide an overview for recent research in small cell networks (SCNs), and then we explore the novel paradigm of proactive caching in SCNs that leverages the latest developments in storage, context-awareness, and social networking. With this approach, we show that important gains can be obtained, with backhaul offloadings and higher ratios of satisfied users reaching up to 22% and 26%, respectively. I. SMALL CELL NETWORKS: PAST, PRESENT AND FUTURE TRENDS Smartphones have exponentially increased the traffic load in current cellular networks showing no signs of slowing down [1], [2]. It is now well understood that a very effective way to increase network capacity is making cells smaller by reducing the distance to the users [3]. Indeed, cell densification has gone from the order of hundreds of square kilometers (back in the eighties) to a fraction of a square meter or less with the advent of hotspots. There has been recently a great interest to deploy relays, distributed antennas and small cellular access points (such as micro/pico/femto cells) in residential homes, subways, enterprises, and hot-spot areas. These network architectures, which are either operator-deployed or user-deployed are referred to heterogeneous networks (HetNets) or small cell networks (SCNs) [3], [4]. By deploying additional network nodes within local-area range and making the network closer to end-users, small cells can significantly improve spatial reuse and coverage, boost capacity, and offload traffic more efficiently [4].

show abstract

Fundamental Limits of Caching

Cited by 1,497 publications

References 15 publications

Coded caching for reducing CSIT-feedback in wireless communications

Coded caching for reducing CSIT-feedback in wireless communications

Distributed caching in device-to-device networks: A stochastic geometry perspective

Proactive Caching in 5G Small Cell Networks

Contact Info

Product

Resources

About