Fundamental Tradeoff between Storage and Latency in Cache-Aided Wireless Interference Networks

TL;DR

This paper characterizes the fundamental tradeoff between storage capacity and latency in cache-enabled wireless interference networks using an information-theoretic metric, proposing optimal and near-optimal strategies for minimizing delivery time.

Contribution

It introduces a novel cooperative caching and delivery strategy that optimally balances storage and latency, with theoretical bounds and practical schemes for various network configurations.

Findings

Achievable NDT matches the lower bound in certain cache regions.

Proposed strategies leverage local caching, coded multicasting, and transmitter cooperation.

Network latency can be significantly reduced with combined caching at transmitters and receivers.

Abstract

This paper studies the fundamental tradeoff between storage and latency in a general wireless interference network with caches equipped at all transmitters and receivers. The tradeoff is characterized by an information-theoretic metric, \emph{normalized delivery time} (NDT), which is the worst-case delivery time of the actual traffic load at a transmission rate specified by degrees of freedom (DoF) of a given channel. We obtain both an achievable upper bound and a theoretical lower bound of the minimum NDT for any number of transmitters, any number of receivers, and any feasible cache size tuple. We show that the achievable NDT is exactly optimal in certain cache size regions, and is within a bounded multiplicative gap to the theoretical lower bound in other regions. In the achievability analysis, we first propose a novel cooperative transmitter/receiver coded caching strategy. It offers the freedom to adjust file splitting ratios for NDT minimization. We then propose a delivery strategy which transforms the considered interference network into a new class of cooperative X-multicast channels. It leverages local caching gain, coded multicasting gain, and transmitter cooperation gain (via interference alignment and interference neutralization) opportunistically. Finally, the achievable NDT is obtained by solving a linear programming problem. This study reveals that with caching at both transmitter and receiver sides, the network can benefit simultaneously from traffic load reduction and transmission rate enhancement, thereby effectively reducing the content delivery latency.