Multi-Server Coded Caching

Shariatpanahi, Seyed Pooya; Motahari, Seyed Abolfazl; Khalaj, Babak Hossein

doi:10.1109/tit.2016.2614722

Cited by 204 publications

(273 citation statements)

References 20 publications

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“…Hence, our result can also be viewed as a generalization of the result in [3] where the cache size of each transmitter may be arbitrarily smaller than the entire library size. Remark 1.…”

Section: Main Results and Its Implicationsmentioning

confidence: 84%

“…Hence, our result subsumes the result of [2] by generalizing it for the case of multiple transmitters. 7) (Connection to multi-server coded caching [3]) Another special case of our network model is the case where each transmitter has space to cache the entire library; i.e., M T = N . This case was previously considered in [3] and it can be verified that in this case, a sum-DoF of min…”

Section: Main Results and Its Implicationsmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9][10][11][12][13]). In particular, in a network with only one transmitter broadcasting to several receivers, it was shown in [2] that local delivery attains only a small fraction of the gain that caching can offer, and by designing a particular pattern in cache placement at the users and exploiting coding in delivery, a significantly larger global throughput gain can be achieved, which is a function of the entire cache throughout the network.…”

Section: Introductionmentioning

confidence: 99%

“…This also demonstrates that the gain of caching scales with the size of the network. As a follow-up, this work has been extended to the case of multiple transmitters in [3], where it was shown that the gain of caching can be improved if several transmitters have access to the entire library of files. Caching at the transmitters was also considered in [4,5] and used to induce collaboration between transmitters in the network.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fundamental Limits of Cache-Aided Interference Management

Naderializadeh

Maddah-Ali

Avestimehr

2017

IEEE Trans. Inform. Theory

197

314

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We consider a system, comprising a library of N files (e.g., movies) and a wireless network with KT transmitters, each equipped with a local cache of size of MT files, and KR receivers, each equipped with a local cache of size of MR files. Each receiver will ask for one of the N files in the library, which needs to be delivered. The objective is to design the cache placement (without prior knowledge of receivers' future requests) and the communication scheme to maximize the throughput of the delivery. In this setting, we show that the sum degrees-of-freedom (sum-DoF) of min, KR is achievable, and this is within a factor of 2 of the optimum, under one-shot linear schemes. This result shows that (i) the one-shot sum-DoF scales linearly with the aggregate cache size in the network (i.e., the cumulative memory available at all nodes), (ii) the transmitters' caches and receivers' caches contribute equally in the one-shot sum-DoF, and (iii) caching can offer a throughput gain that scales linearly with the size of the network.To prove the result, we propose an achievable scheme that exploits the redundancy of the content at transmitters' caches to cooperatively zero-force some outgoing interference, and availability of the unintended content at the receivers' caches to cancel (subtract) some of the incoming interference. We develop a particular pattern for cache placement that maximizes the overall gains of cache-aided transmit and receive interference cancellations. For the converse, we present an integer optimization problem which minimizes the number of communication blocks needed to deliver any set of requested files to the receivers. We then provide a lower bound on the value of this optimization problem, hence leading to an upper bound on the linear one-shot sum-DoF of the network, which is within a factor of 2 of the achievable sum-DoF.

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“…Hence, our result can also be viewed as a generalization of the result in [3] where the cache size of each transmitter may be arbitrarily smaller than the entire library size. Remark 1.…”

Section: Main Results and Its Implicationsmentioning

confidence: 84%

Section: Main Results and Its Implicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fundamental Limits of Cache-Aided Interference Management

Naderializadeh

Maddah-Ali

Avestimehr

2017

IEEE Trans. Inform. Theory

197

314

View full text Add to dashboard Cite

show abstract

“…The basic coded caching strategy proposed in [1] has been extended to a variety of other networking scenarios, among which are decentralized [2], multi-server [3], hierarchical [4], multi-request [5] and online coded caching [6], and caching with heterogenous cache sizes [7]. Perhaps the most relevant to our work is "multi-level coded caching" [8], [9] where several popularity classes of files are served to the users via access points that are in possession of local caches.…”

Section: Introductionmentioning

confidence: 99%

Multi-library coded caching

Sahraei

Gastpar

2016

2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Abstract-We study the problem of coded caching when the server has access to several libraries and each user makes independent requests from every library. The single-library scenario has been well studied and it has been proved that coded caching can significantly improve the delivery rate compared to uncoded caching. In this work we show that when all the libraries have the same number of files, memory-sharing is optimal and the delivery rate cannot be improved via coding across files from different libraries. In this setting, the optimal memory-sharing strategy is one that divides the cache of each user proportional to the size of the files in different libraries. As for the general case, when the number of files in different libraries are arbitrary, we propose an inner-bound based on memory-sharing and an outerbound based on concatenation of files from different libraries.

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Proactive Caching in 5G Small Cell Networks

2016

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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].

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Multi-Server Coded Caching

Cited by 204 publications

References 20 publications

Fundamental Limits of Cache-Aided Interference Management

Fundamental Limits of Cache-Aided Interference Management

Multi-library coded caching

Proactive Caching in 5G Small Cell Networks

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