Collaborative Coded Caching for Partially Connected Networks

TL;DR

This paper introduces a novel coded caching delivery scheme for partially connected networks modeled as MIMO Gaussian broadcast channels, using partitioning and multicast transmission to improve efficiency.

Contribution

It proposes a new two-phase delivery scheme with optimal user partitioning and heuristic transmission, applicable to any partially connected network.

Findings

Performance closely matches fully connected optimal solutions

Partitioning minimizes the number of user sets for decoding

Heuristic transmission achieves near-optimal results

Abstract

Coded caching leverages the differences in user cache memories to achieve gains that scale with the total cache size, alleviating network congestion due to high-quality content requests. Additionally, distributing transmitters over a wide area can mitigate the adverse effects of path loss. In this work, we consider a partially connected network where the channel between distributed transmitters (helpers) and users is modeled as a distributed multiple-input-multiple-output (MIMO) Gaussian broadcast channel. We propose a novel delivery scheme consisting of two phases: partitioning and transmission. In the partitioning phase, users with identical cache profiles are partitioned into the minimum number of sets, such that users within each set can successfully decode their desired message from a joint transmission enabled by MIMO precoding. To optimally partition the users, we employ the branch and bound method. In the transmission phase, each partition is treated as a single entity, and codewords are multicast to partitions with distinct cache profiles. The proposed delivery scheme is applicable to any partially connected network, and while the partitioning is optimal, the overall delivery scheme, including transmission, is heuristic. Interestingly, simulation results show that its performance closely approximates that of the fully connected optimal solution.