Cache-Aided General Linear Function Retrieval

• Main Authors: Kai Wan, Hua Sun, Mingyue Ji, Daniela Tuninetti, Giuseppe Caire
• Format: Article
• Language: English
• Published: MDPI AG 2021-12-01
• Series: Entropy
• Subjects: coded caching; linear function retrieval; uncoded cache placement
• Online Access: https://www.mdpi.com/1099-4300/23/1/25

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.