Coded Caching for Wireless Backhaul Networks With Unequal Link Rates

Tang, Aimin; Roy, Sumit; Wang, Xudong

doi:10.1109/tcomm.2017.2746106

Cited by 45 publications

(43 citation statements)

References 30 publications

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“…Coded caching has been a research focus during recent years [5][6][7][8][9]. Coded caching is finding its application in modern technologies and services, such as content delivery [10][11][12], mobile computing [13][14][15], and information-enteric networks [16]. Different aspects of coded caching have recently been studied among which we can refer to as [17], centralized [18,19] and decentralized [18,19] coded caching, placement [20] and delivery [21,22] schemes, as well as added pre-fetching phase [23], multi-casting [22,[24][25][26], scheduling [27], error correction [28], clustering [29], heterogeneity [12,25,30], the impact of file size [31,32], dealing with non-uniform user demands [33] and peak-time traffic reduction [1].…”

Section: Related Workmentioning

confidence: 99%

Cryptography in Hierarchical Coded Caching: System Model and Cost Analysis

Zolfaghari

Singh

Kumar

et al. 2021

Entropy

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The idea behind network caching is to reduce network traffic during peak hours via transmitting frequently-requested content items to end users during off-peak hours. However, due to limited cache sizes and unpredictable access patterns, this might not totally eliminate the need for data transmission during peak hours. Coded caching was introduced to further reduce the peak hour traffic. The idea of coded caching is based on sending coded content which can be decoded in different ways by different users. This allows the server to service multiple requests by transmitting a single content item. Research works regarding coded caching traditionally adopt a simple network topology consisting of a single server, a single hub, a shared link connecting the server to the hub, and private links which connect the users to the hub. Building on the results of Sengupta et al. (IEEE Trans. Inf. Forensics Secur., 2015), we propose and evaluate a yet more complex system model that takes into consideration both throughput and security via combining the mentioned ideas. It is demonstrated that the achievable rates in the proposed model are within a constant multiplicative and additive gap with the minimum secure rates.

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Section: Related Workmentioning

confidence: 99%

Cryptography in Hierarchical Coded Caching: System Model and Cost Analysis

Zolfaghari

Singh

Kumar

et al. 2021

Entropy

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“…Further details of this approximately uniform partitioning are provided in Appendix A. We note that, if Λ i, j ⊆ F i, j , then (7) holds. We also assume that the same partitioning is applied to all Λ i 's.…”

Section: Delivery Phasementioning

confidence: 99%

Uncoded Caching and Cross-Level Coded Delivery for Non-Uniform File Popularity

Ozfatura

Guenduez

2018

2018 IEEE International Conference on Communications (ICC)

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Proactive content caching at user devices and coded delivery is studied for a non-uniform file popularity distribution. A novel centralized uncoded caching and coded delivery scheme is proposed, which can be applied to large file libraries with nonuniform demands. A special case of the more general polynomialcomplexity cross-level coded delivery (CLCD) scheme is presented in this paper, and it is shown to achieve a lower average delivery rate than the state of art. In the proposed scheme, the same subpacketization is used for all the files in the library in order to prevent additional zero-padding in the delivery phase. In CLCD, unlike existing schemes in the literature, two users requesting files from different popularity groups can still be served by the same multicast message in order to reduce the delivery rate.Simulation results indicate significant reduction in the average delivery rate for typical Zipf distribution parameter values.

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“…The works [7]- [9] consider the probabilistic caching that SBSs cache files according to certain welldesigned probabilities. The authors in [10]- [12] consider the coded caching that files are partitioned into multiple segments, X. Xu and these segments after coding are cached at SBSs.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching

Tao

Shen

2020

IEEE Trans. Wireless Commun.

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This paper investigates learning-based caching in small-cell networks (SCNs) when user preference is unknown. The goal is to optimize the cache placement in each small base station (SBS) for minimizing the system long-term transmission delay. We model this sequential multi-agent decision making problem in a multi-agent multi-armed bandit (MAMAB) perspective. Rather than estimating user preference first and then optimizing the cache strategy, we propose several MAMAB-based algorithms to directly learn the cache strategy online in both stationary and non-stationary environment. In the stationary environment, we first propose two high-complexity agent-based collaborative MAMAB algorithms with performance guarantee. Then we propose a low-complexity distributed MAMAB which ignores the SBS coordination. To achieve a better balance between SBS coordination gain and computational complexity, we develop an edge-based collaborative MAMAB with the coordination graph edge-based reward assignment method. In the non-stationary environment, we modify the MAMAB-based algorithms proposed in the stationary environment by proposing a practical initialization method and designing new perturbed terms to adapt to the dynamic environment. Simulation results are provided to validate the effectiveness of our proposed algorithms. The effects of different parameters on caching performance are also discussed.

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Coded Caching for Wireless Backhaul Networks With Unequal Link Rates

Cited by 45 publications

References 30 publications

Cryptography in Hierarchical Coded Caching: System Model and Cost Analysis

Cryptography in Hierarchical Coded Caching: System Model and Cost Analysis

Uncoded Caching and Cross-Level Coded Delivery for Non-Uniform File Popularity

Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching

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