Collaborative Coded Caching for Partially Connected Networks

2025-01-23

AI Summary

To address the low transmission efficiency of high-bandwidth services (e.g., HD video) in partially connected distributed MIMO broadcast networks—caused by channel fading and heterogeneous cache distributions—this paper proposes a two-stage cooperative coded-caching transmission scheme. First, users are optimally grouped based on their cache content characteristics using a branch-and-bound algorithm; second, joint beamforming broadcast strategies are designed per group. This work is the first to deeply integrate cache-driven optimal user grouping with MIMO joint precoding under partial connectivity constraints, thereby approaching the theoretical caching gain achievable in fully connected networks. Simulation results demonstrate that the proposed method closely approaches the full-connectivity performance bound, significantly outperforming conventional single-point transmission and non-grouped caching strategies. It effectively alleviates performance bottlenecks induced by path loss and uneven cache distribution.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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 MIMO Gaussian broadcast channel. We propose a novel delivery scheme consisting of two phases: emph{partitioning} and emph{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.

Problem

• Network Congestion
• Buffer Efficiency
• Distributed MIMO Broadcast Networks

Innovation

• Distributed MIMO Broadcasting
• Cache-aware Encoding
• Adaptive Signal Transmission