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A General Coded Caching Scheme for Scalar Linear Function Retrieval

2021/02/03 by Yinbin Ma, Ma, Yinbin, Daniela Tuninetti +1
Computer Science · #Caching and Content Delivery #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Information Theory (cs.IT) #Stochastic Gradient Optimization Techniques

paper · pdf · doi:10.48550/arxiv.2102.02122

openalex publication_date 2021/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Coded caching aims to minimize the network's peak-time communication load by leveraging the information pre-stored in the local caches at the users. The original single file retrieval setting by Maddah-Ali and Niesen has been recently extended to general Scalar Linear Function Retrieval (SLFR) by Wan et al., who proposed a linear scheme that surprisingly achieves the same optimal load (under the constraint of uncoded cache placement) as in single file retrieval. This paper's goal is to characterize the conditions under which a general SLFR linear scheme is optimal and gain practical insights into why the specific choices made by Wan et al. work. This paper shows that the optimal decoding coefficients are necessarily the product of two terms, one only involving the encoding coefficients and the other only the demands. In addition, the relationships among the encoding coefficients are shown to be captured by the cycles of certain graphs. Thus, a general linear scheme for SLFR can be found by solving a spanning tree problem.

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