Edge Caching for Cache Intensity under Probabilistic Delay Constraint

TL;DR

This paper investigates the trade-offs between base station densification and cache size at the network edge to optimize data delivery delay constraints, using stochastic geometry and geometric programming.

Contribution

It introduces a novel cache intensity metric under probabilistic delay constraints and provides analytical solutions for optimizing BS density and cache size.

Findings

Optimal cache intensity balances BS densification and cache size.

Analytical solutions for nonconvex optimization problems.

Trade-off analysis between network deployment and caching.

Abstract

In order to reduce the latency of data delivery, one of techniques is to cache the popular contents at the base stations (BSs) i.e. edge caching. However, the technique of caching at edge can only reduce the backhaul delay, other techniques such as BS densification will also need to be considered to reduce the fronthaul delay. In this work, we study the trade-offs between BS densification and cache size under delay constraint at a typical user (UE). For this, we use the downlink SINR coverage probability and throughput obtained based on stochastic geometrical analysis. The network deployment of BS and cache storage is introduced as a minimization problem of the product of the BS intensity and cache size which we refer to the product of \tit{cache intensity}' under probabilistic delay constraint. We examine the cases when (i) either BS intensity or the cache size is held fixed, and (ii) when both BS intensity and the cache size are vary. For the case when both BS intensity and the cache size are variable, the problem become nonconvex and we convert into a geometric programing which we solve it analytically.