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

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.