On Coded Caching Systems with Decentralized Linear Coding Placement

2026-04-29

AI Summary

This work addresses the challenge of optimizing communication load in decentralized coded caching systems, where the lack of coordination during cache placement complicates performance guarantees. For the first time, it characterizes the fundamental information-theoretic limits of worst-case communication load under a model where users independently cache random linear combinations of encoded symbols. By integrating random linear coding, information-theoretic converse techniques, and combinatorial optimization, the authors devise efficient caching and delivery strategies and derive matching upper and lower bounds on the achievable load. Under specific parameter regimes, these bounds coincide exactly, thereby establishing the optimal communication performance and marking a significant theoretical advance in decentralized coded caching.

Abstract

Coded caching is a technique that leverages locally cached contents at the end users to reduce the network's peak-time communication load. Coded caching has been shown to achieve significant performance gains with a centralized placement orchestrated by the server and is thus considered a promising technique to boost performance in future networks by effectively trading off bandwidth for storage. To tackle issues caused by the synchronized placement, previous works focused on decentralized placement and found the exact worst-case load with uncoded placement. In this paper, we focus on a decentralized coded caching system with random linear coding placement, and investigate the fundamental limits of a linear coding placement where each user independently and uniformly caches random linear coding symbols of a single file. We propose achievable and converse bounds on the worst-case load, which are shown to meet under certain conditions.

Problem

• coded caching
• decentralized placement
• linear coding
• communication load
• fundamental limits

Innovation

• coded caching
• decentralized placement
• linear coding
• worst-case load
• converse bound