A stochastic geometry approach to distributed caching in large wireless networks

Abstract

International audienceThis paper introduces a stochastic geometry model of a cellular network in which users exchange videos through out-of-band device-to-device (D2D) communications. Users are grouped into clusters, in which a user-based distributed cache is formed to satisfy in-cluster video requests through D2D, avoiding the base station. This paper studies how many of these requests could be served, considering two service metrics: the global relative density of served requests, and the mean ratio of served requests per cluster. The model considers random user placement, content request statistics, and transmission scheduling and impairments, like fading and path loss. A simple communications strategy to be used in the clusters, involving time-division multiple access and one-hop transmissions, is introduced and the metrics are evaluated in this setup, drawing conclusions on the performance of distributed-caching in large wireless networks

A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission

“…They decompose the problem into three sub-problems and solve each sub-problem in a centralized manner using convex optimization. D2Dcaching is also the topic of [8]. Assuming that the knowledge of the network's topology and files' popularity is available, the authors use stochastic geometry to design communication protocols.…”

“…Probabilistic cache placement for D2D-caching is discussed in [9]. Similar to [8], the authors assume that a central controller knows the network's topology and files' popularity. Based on this assumption, they propose a solution to the formulated problem.…”

“…Let μf,θ−1 be the estimated value of µ f,θ−1 at the end of broadcast round θ−1. 8 To update the cache for the broadcast round θ, the SBS uses the historical data. Hence the optimization problem ( 24) is restated as…”

“…Let Q f,m,1:T = (q f,m,1 , ..., q f,m,T ) be a set of samples of the popularity of file f ∈ F , where q f,m,t shows the number of requests for file f submitted to SBS m at time j = 1, ..., T . Assume that the popularity is 8 It is worth noting that in some previous works, for example, in [27] and [24], the authors assume the following: Every SBS observes the cache hits, i.e., the number of requests for the files in the cache, whereas the number of cache drops, i.e., the number of requests for the files that do not belong to the cache, is not observable. However, such an assumption is unnecessary in many cases, where the users submit the requests for files to the corresponding SBS, while not knowing the cache status of the SBS.…”

A Device-to-Device Multicast Scheme for Delay-Constraint Content Delivery

“…To introduce D2D content delivery, some beginning works have been done such as the design of architecture, caching, and delivery strategy to realize proactive caching and intelligent delivery at UE. For example, by assuming that D2D UE with common interests constructs a cluster for D2D content sharing, the average number of served requests per cluster and optimal cluster size are analyzed in [9,10]. A tractable closed-form equation to find when redundant caching should be used in order to minimize the expected energy consumption was derived in [11].…”