This work identifies the fundamental limits of Multi-Access Coded Caching (MACC) where each user is connected to multiple caches in a manner that follows a generalized combinatorial topology. This topology stands out as it allows for unprecedented coding gains, even with very modest cache resources. First, we extend the setting and the scheme presented by Muralidhar et al. to a much more general topology that supports both a much denser range of users and the coexistence of users connected to different numbers of caches, all while maintaining the astounding coding gains, here proven to be exactly optimal, associated with the combinatorial topology. This is achieved, for this generalized topology, with a novel information-theoretic converse that we present here, which establishes, together with the scheme, the exact optimal performance under the assumption of uncoded placement. We subsequently consider different connectivity ensembles, including the very general scenario of the entire ensemble of all possible network connectivities/topologies, where any user can be connected to any subset of caches arbitrarily. For these settings, we develop novel converse bounds on the optimal performance averaged over the ensemble's different connectivities, considering the additional realistic scenario that the connectivity at delivery time is entirely unknown during cache placement. This novel analysis of topological ensembles leaves open the possibility that currently-unknown topologies may yield even higher gains, a hypothesis that is part of the bigger question of which network topology yields the most caching gains.
翻译:这项工作确定了多入代码化缓存( MACC) 的基本限制, 每一个用户都与多个缓存连接到多个缓存处, 其基本限制是随一个普遍的组合式地形学而关联的。 这个表层学表现突出, 因为它允许史无前例的编码增益, 即使使用非常小的缓存资源。 首先, 我们将Muralidhar et al. 提供的设置和计划扩展至一个更一般性的表层学, 既支持更密集的用户范围, 也支持用户与不同数目的缓存处连接在一起的用户的共存, 同时又保持惊人的编码增益, 这里证明是非常理想的, 与组合式表层表层学相联。 对于这个普遍的表层学来说, 我们在这里展示了一个新的信息- 理论性对调和调, 与这个计划一起, 在未编码化的放置假设下, 我们考虑不同的连接层层层层层的精确性变现, 考虑的是, 最高层层层层层层变的变平, 最佳性变平, 的图像变平的变平。 考虑最接近于最接近性变平的图像, 的图像变平, 的上层变平局性变平, 的变平, 的变平, 变平局变平。