Cache-aided General Linear Function Retrieval
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.
Keywords
Cite
@article{arxiv.2012.14394,
title = {Cache-aided General Linear Function Retrieval},
author = {Kai Wan and Hua Sun and Mingyue Ji and Daniela Tuninetti and Giuseppe Caire},
journal= {arXiv preprint arXiv:2012.14394},
year = {2021}
}
Comments
21 pages, 4 figures, published in Entropy 2021, 23(1), 25