Fundamental Limits of Caching in Wireless D2D Networks

By Mingyue Ji, Giuseppe Caire, Andreas F. Molisch. IEEE Transactions on Information Theory, 2014

601 citations recorded by the source corpus.

We consider a wireless device-to-device (D2D) network where communication is restricted to be single-hop. Users make arbitrary requests from a finite library of files and have pre-cached information on their devices, subject to a per-node storage capacity constraint. A similar problem has already been considered in an infrastructure setting, where all users receive a common multicast (coded) message from a single omniscient server (e.g., a base station having all the files in the library) through a shared bottleneck link. In this paper, we consider a D2D infrastructureless version of the problem. We propose a caching strategy based on deterministic assignment of subpackets of the library files, and a coded delivery strategy where the users send linearly coded messages to each other in order to collectively satisfy their demands. We also consider a random caching strategy, which is more suitable to a fully decentralized implementation. Under certain conditions, both approaches can achieve the information theoretic outer bound within a constant multiplicative factor. In our previous work, we showed that a caching D2D wireless network with one-hop communication, random caching, and uncoded delivery (direct file transmissions) achieves the same throughput scaling law of the infrastructure-based coded multicasting scheme, in the regime of large number of users and files in the library. This shows that the spatial reuse gain of the D2D network is order-equivalent to the coded multicasting gain of single base station transmission. It is, therefore, natural to ask whether these two gains are cumulative, i.e., if a D2D network with both local communication (spatial reuse) and coded multicasting can provide an improved scaling law. Somewhat counterintuitively, we show that these gains do not cumulate (in terms of throughput scaling law). This fact can be explained by noticing that the coded delivery scheme creates messages that are useful to multiple nodes, such that it benefits from broadcasting to as many nodes as possible, while spatial reuse capitalizes on the fact that the communication is local, such that the same time slot can be reused in space across the network. Unfortunately, these two issues are in contrast with each other.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/3d601571fdf082b3f2bbf16befbeaa6702664dff

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Citations in the corpus

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions (2017, background): “In [170], two caching and delivery schemes are considered.”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “, wireless edge caching, stands out as a promising solution for efficient content delivery [32], [33], [34], [35].”
Coded MapReduce (2015, background): “The idea of efficiently creating and exploiting coded multicasting was initially proposed in the context of cache networks in [11], [12], and extended in [13], [14], where caches pre-fetch part of the content in a way that coding opportunities can be optimally exploited to reduce network traffic dur”
Wireless Device-to-Device Caching Networks: Basic Principles and System Performance (2013, background): “6Interestingly, in the recent work [41], the authors showed that the gain of spatial reuse from uncoded D2D caching scheme and the gain of the coded multicasting do not accumulate in the order sense.”
The Exact Rate-Memory Tradeoff for Caching With Uncoded Prefetching (2016, background): “The caching problem has also been extended in various directions, including decentralized caching [17], online caching [18], caching with nonuniform demands [19]–[22], hierarchical caching [23]–[25], device-to-device caching [26], cache-aided interference channels [27]–[30], caching on file selectio”
Wireless caching: technical misconceptions and business barriers (2016, background): “This promising idea has sprung a wealth of research efforts, some of them in parallel or right after, such as device-to-device (D2D) networks [30], [31], non-uniform content popularities [32], online caching policies [33], multi-servers [34], multi-library [35] and multihop wireless networks with co”
Ultra-Dense Networks: A Survey (2016, background): “However, the fundamental limits of the predictive caching need to be investigated to assess the role of different parameters and to promote effective design techniques [147], [151].”
Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities (2017, background): “Finally, recent studies have investigated the role of coding for enhancing performance in cache-enabled networks [18]–[20]; see also [21], [22], and [23], where device-to-device “structure-”
Cache-enabled small cell networks: modeling and tradeoffs (2014, background): “The performance of caching in wireless device-to-device networks is studied in [22] in a scenario where nodes are placed on a grid and cache the content randomly.”
A Fundamental Tradeoff Between Computation and Communication in Distributed Computing (2016, methodology): “For the case of s = 1 where no two nodes are interested in computing a common Reduce function, the coded data shuffling of CDC is similar to a coded transmission strategy in wireless D2D networks proposed in [22], where the side information enabling coded multicasting are pre-fetched in a specific r”
Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning (2018, background): “It can cache popular contents at various nearby devices [11].”