Fundamental Limits of Caching
TLDR
This paper proposes a novel coded caching scheme that exploits both local and global caching gains, leading to a multiplicative improvement in the peak rate compared with previously known schemes, and argues that the performance of the proposed scheme is within a constant factor of the information-theoretic optimum for all values of the problem parameters.Abstract:
Caching is a technique to reduce peak traffic rates by prefetching popular content into memories at the end users. Conventionally, these memories are used to deliver requested content in part from a locally cached copy rather than through the network. The gain offered by this approach, which we term local caching gain, depends on the local cache size (i.e., the memory available at each individual user). In this paper, we introduce and exploit a second, global, caching gain not utilized by conventional caching schemes. This gain depends on the aggregate global cache size (i.e., the cumulative memory available at all users), even though there is no cooperation among the users. To evaluate and isolate these two gains, we introduce an information-theoretic formulation of the caching problem focusing on its basic structure. For this setting, we propose a novel coded caching scheme that exploits both local and global caching gains, leading to a multiplicative improvement in the peak rate compared with previously known schemes. In particular, the improvement can be on the order of the number of users in the network. In addition, we argue that the performance of the proposed scheme is within a constant factor of the information-theoretic optimum for all values of the problem parameters.read more
Citations
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Proceedings ArticleDOI
Reinforcement Learning for Proactive Caching of Contents with Different Demand Probabilities
TL;DR: Comparisons with two performance lower bounds, one computed based on infinite cache capacity and another based on non-casual knowledge of the user access times and content requests, demonstrate that the resulting scheme can perform close to the theoretical optimum.
Posted Content
Optimal Geographical Caching in Heterogeneous Cellular Networks with Nonhomogeneous Helpers.
Berksan Serbetci,Jasper Goseling +1 more
TL;DR: This work provides a distributed local optimization algorithm (LOA) that finds the optimal placement policies for different types of BSs and shows that storing the most popular content in the MBSs is almost optimal if the SBSs are using optimal placement policy.
Posted Content
Key Superposition Simultaneously Achieves Security and Privacy in Cache-Aided Linear Function Retrieval
Qifa Yan,Daniela Tuninetti +1 more
TL;DR: A coded caching scheme, referred to as key superposition, is proposed in the cache-aided content Secure and demand Private Linear Function Retrieval (SP-LFR) setup, which uses the superposition of security keys and privacy keys in both the placement and delivery phases to guarantee content security and demand privacy.
Proceedings ArticleDOI
Optimal decentralized coded caching for heterogeneous files
TL;DR: A novel optimization strategy for coded caching is proposed that minimizes the worst-case transmission rate of multicasting the coded content upon users requests, subject to the storage constraint at the local caches, by the optimal allocation of the caching proportion among heterogeneous files.
Proceedings ArticleDOI
Complete interference mitigation through receiver-caching in Wyner's networks
TL;DR: In this article, upper and lower bounds on the per-user multiplexing gain (MG) of Wyner's circular soft-handoff model with cognitive transmitters and receivers with cache memories were presented.
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