Multiaccess Coded Caching with Heterogeneous Retrieval Costs

TL;DR

This paper introduces a cost-aware multiaccess coded caching framework that accounts for heterogeneous retrieval costs, optimizing cache placement and transmission to minimize overall system costs.

Contribution

It proposes a superposition coding-based caching scheme and a structure-aware optimization algorithm for cost minimization in heterogeneous environments.

Findings

Outperforms existing schemes in heterogeneous cost scenarios.

Demonstrates the effectiveness of superposition coding in cost-aware caching.

Provides a reduced-complexity algorithm for practical implementation.

Abstract

The multiaccess coded caching (MACC) system, as formulated by Hachem {\it et al.}, consists of a central server with a library of $N$ files, connected to $K$ cache-less users via an error-free shared link, and $K$ cache nodes, each equipped with cache memory of size $M$ files. Each user can access $L$ neighboring cache nodes under a cyclic wrap-around topology. Most existing studies operate under the strong assumption that users can retrieve content from their connected cache nodes at no communication cost. In practice, each user retrieves content from its $L$ different connected cache nodes at varying costs. Additionally, the server also incurs certain costs to transmit the content to the users. In this paper, we focus on a cost-aware MACC system and aim to minimize the total system cost, which includes cache-access costs and broadcast costs. Firstly, we propose a novel coded caching framework based on superposition coding, where the MACC schemes of Cheng \textit{et al.} are layered. Then, a cost-aware optimization problem is derived that optimizes cache placement and minimizes system cost. By identifying a sparsity property of the optimal solution, we propose a structure-aware algorithm with reduced complexity. Simulation results demonstrate that our proposed scheme consistently outperforms the scheme of Cheng {\it et al.} in scenarios with heterogeneous retrieval costs.