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arXiv · 2012.14394

Cache-aided General Linear Function Retrieval

Kai Wan·Hua Sun·Mingyue Ji·Daniela Tuninetti·Giuseppe Caire
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Abstract

Coded Caching, proposed by Maddah-Ali and Niesen (MAN), has the potential to reduce network traffic by pre-storing content in the users' local memories when the network is underutilized and transmitting coded multicast messages that simultaneously benefit many users at once during peak-hour times. This paper considers the linear function retrieval version of the original coded caching setting, where users are interested in retrieving a number of linear combinations of the data points stored at the server, as opposed to a single file. This extends the scope of the Authors' past work that only considered the class of linear functions that operate element-wise over the files. On observing that the existing cache-aided scalar linear function retrieval scheme does not work in the proposed setting, this paper designs a novel coded caching scheme that outperforms uncoded caching schemes that either use unicast transmissions or let each user recover all files in the library.

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Kai Wan, Hua Sun, Mingyue Ji, Daniela Tuninetti, Giuseppe Caire. 2020-12-28. Cache-aided General Linear Function Retrieval. https://doi.org/10.3390/e23010025

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