Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Wireless Networks

Cui, Ying; Jiang, Dongdong; Wu, Yueping

doi:10.1109/twc.2016.2553023

Cited by 120 publications

(163 citation statements)

References 21 publications

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“…Each SBS is equipped with a cache of size M (in files), where M ≤ N, and can serve any files stored locally. To provide spatial file diversity (which can improve performance of dense wireless networks) [23], we adopt a random caching scheme at SBSs. In particular, each SBS stores M different files out of all N files in N with a certain probability.…”

Section: System Modelmentioning

confidence: 99%

“…In addition, due to the indicator function ½[•] in ψ sch 1 (T), the objective function ψ sch 1 (T) is not differentiable w.r.t. T, which means that we cannot directly apply the standard gradient projection method in [23] to obtain a local optimal solution of Problem 1 numerically. In the following, we construct an equivalent problem of Problem 1 by making use of the optimality properties in Lemma 1.…”

Section: Remark 3 (Interpretation Of Lemma 1)mentioning

confidence: 99%

“…In general, it is difficult to show the convexity of ψ s 1 (T n ) in (10) and hence the convexity of P s (T, N s ). Since P s (T, N s ) is differentiable, we can apply the standard gradient projection method, e.g., Algorithm 1 in [23], to obtain a local optimal solution to the optimization in (21).…”

Section: Problem 3 (Equivalent Problem Of Problem 2)mentioning

confidence: 99%

“…In [21] and [22], the authors consider random caching at SBSs, and analyze and optimize the cache hit probability (i.e., the probability that a randomly requested file from the typical user is stored at its serving BS) [21] and successful offloading probability (i.e., the probability that the typical user is associated with the SBS tier and its downlink signal-to-interference-plus-noise ratio is larger than a threshold) [22]. In [23], the authors consider random caching and multicasting at SBSs in a large-scale small cell network, and analyze and optimize the successful transmission probability (i.e., the probability that a randomly requested file from the typical user can be successfully received). In [24], the authors propose a hybrid caching design (consisting of identical caching in the MBS tier and random caching in the SBS tier) and a corresponding multicasting design in a large-scale HetNet, and analyze and optimize the successful transmission probability.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, a local optimal solution to Problem 4 can be obtained using gradient projection method [23].…”

mentioning

confidence: 99%

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Random caching based cooperative transmission in heterogeneous wireless networks

Wen

Cui

Zheng

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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Base station cooperation in heterogeneous wireless networks (HetNets) is a promising approach to improve the network performance, but it also imposes a significant challenge on backhaul. On the other hand, caching at small base stations (SBSs) is considered as an efficient way to reduce backhaul load in HetNets. In this paper, we jointly consider SBS caching and cooperation in a downlink largescale HetNet. We propose two SBS cooperative transmission schemes under random caching at SBSs with the caching distribution as a design parameter. Using tools from stochastic geometry and adopting appropriate integral transformations, we first derive a tractable expression for the successful transmission probability under each scheme. Then, under each scheme, we consider the successful transmission probability maximization by optimizing the caching distribution, which is a challenging optimization problem with a non-convex objective function. By exploring optimality properties and using optimization techniques, under each scheme, we obtain a local optimal solution in the general case and global optimal solutions in some special cases. Compared with some existing caching designs in the literature, e.g., the most popular caching, the i.i.d. caching and the uniform caching, the optimal random caching under each scheme achieves better successful transmission probability performance. The analysis and optimization results provide valuable design insights for practical HetNets.

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Section: System Modelmentioning

confidence: 99%

Section: Remark 3 (Interpretation Of Lemma 1)mentioning

confidence: 99%

Section: Problem 3 (Equivalent Problem Of Problem 2)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this case, a local optimal solution to Problem 4 can be obtained using gradient projection method [23].…”

mentioning

confidence: 99%

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Random caching based cooperative transmission in heterogeneous wireless networks

Wen

Cui

Zheng

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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Deep learning-based edge caching for multi-cluster heterogeneous networks

Yang

Zhang

et al. 2019

Neural Comput & Applic

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In this work, we consider a time and space evolution cache refreshing in multicluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time-space-evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the Deep Neural Network to avoid the repeated calculation because of the change of content popularity distribution at different time slot.

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Mode Selection of Content Delivery at Edge Nodes Based on Learning

Tang

2022

Wireless Pers Commun

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Pre-caching at edge nodes may improve resource efficiency for future content-centric cellular networks by bringing contents closer to users. However, which mode (muticast or unicast) should be selected for the efficient delivery of those cached contents is still not well addressed, especially at the situation that some key parameters such as content popularity and transmission environments are unknown. To solve this problem, the criterion of delivery mode selection is studied based on the learning policy of multi-armed bandit (MAB). According to the criterion, a mode selection algorithm is proposed. In this algorithm, edge nodes can choose the better delivery modes in the current slot only dependent on existing observations in the previous slot. Performance evaluation results validate our analyses and proposals on the mode selection.

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Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Wireless Networks

Cited by 120 publications

References 21 publications

Random caching based cooperative transmission in heterogeneous wireless networks

Random caching based cooperative transmission in heterogeneous wireless networks

Deep learning-based edge caching for multi-cluster heterogeneous networks

Mode Selection of Content Delivery at Edge Nodes Based on Learning

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