Multi-Transmitter Coded Caching Networks With Transmitter-Side Knowledge of File Popularity

This work presents a new way of exploiting non-uniform file popularity in coded caching networks. Focusing on a fully-connected fully-interfering wireless setting with multiple cache-enabled transmitters and receivers, we show how non-uniform file popularity can be used very efficiently to accelerate the impact of transmitter-side data redundancy on receiver-side coded caching. This approach is motivated by the recent discovery that, under any realistic file-size constraint, having content appear in multiple transmitters can in fact dramatically boost the speed-up factor attributed to coded caching. We formulate an optimization problem that exploits file popularity to optimize the placement of files at the transmitters. Consequently, we propose a search algorithm that solves the problem at hand while reducing the variable search space significantly. We also prove an analytical performance upper bound, which is in fact met by our algorithm in the regime of many receivers. Our work reflects the benefits of allocating higher cache redundancy to more popular files, but also reflects a law of diminishing returns where for example very popular files may in fact benefit from minimum redundancy. In the end, this work reveals that in the context of coded caching, employing multiple transmitters can be a catalyst in fully exploiting file popularity, as it avoids various asymmetry complications that appear when file popularity is used to alter the receiver-side cache placement.

Automated summary

This work presents a new approach to accelerate the impact of transmitter-side data redundancy on receiver-side coded caching by exploiting non-uniform file popularity. It formulates an optimization problem and proposes a search algorithm to optimize the placement of files based on their popularity. The study demonstrates that employing multiple transmitters can fully exploit file popularity and avoid complications arising from altering cache placement based on file popularity.