A Low-Complexity Architecture for Multi-access Coded Caching Systems with Arbitrary User-cache Access Topology

2026-01-15

AI Summary

This work proposes a general graph-based transmission design framework for multi-access coded caching systems under arbitrary user-cache access topologies, reformulating the coded multicast problem as a conflict graph coloring task. For the first time, graph neural networks are leveraged to achieve low-complexity near-optimal solutions, and the index coding duality bound is extended to arbitrary access structures. The proposed learning-based scheme achieves transmission loads close to those of the DSatur greedy algorithm and the theoretical duality bound, while substantially reducing computational overhead, thereby demonstrating strong scalability and adaptability in large-scale and heterogeneous network topologies.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

This paper studies the multi-access coded caching (MACC) problem under arbitrary user-cache access topologies, extending existing models that rely on highly structured and combinatorially designed connectivity. We consider a MACC system consisting of a single server, multiple cache nodes, and multiple user nodes. Each user can access an arbitrary subset of cache nodes to retrieve cached content. The objective is to design a general and low-complexity delivery scheme under fixed cache placement for arbitrary access topologies. We propose a universal graph-based framework for modeling the MACC delivery problem, where decoding conflicts among requested packets are captured by a conflict graph and the delivery design is reduced to a graph coloring problem. In this formulation, a lower transmission load corresponds to using fewer colors. The classical greedy coloring algorithm DSatur achieves a transmission load close to the index-coding converse bound, providing a tight benchmark, but its computational complexity becomes prohibitive for large-scale graphs. To overcome this limitation, we develop a learning-based framework using graph neural networks that efficiently constructs near-optimal coded multicast transmissions and generalizes across diverse access topologies and varying numbers of users. In addition, we extend the index-coding converse bound for uncoded cache placement to arbitrary access topologies and propose a low-complexity greedy approximation. Numerical results demonstrate that the proposed learning-based scheme achieves transmission loads close to those of DSatur and the converse bound while significantly reducing computational time.

Problem

• multi-access coded caching
• arbitrary user-cache access topology
• low-complexity delivery
• cache placement
• transmission load

Innovation

• multi-access coded caching
• arbitrary access topology
• graph neural networks
• conflict graph coloring
• index coding converse bound