Artificial Intelligence Assisted Collaborative Edge Caching in Small Cell Networks

TL;DR

This paper introduces an AI-assisted collaborative caching approach for small cell networks that considers user preferences and heterogeneous cache models, using a modified particle swarm optimization algorithm to improve cache hit ratios.

Contribution

It presents a novel heterogeneous caching model with user preferences and a collaborative framework, solved efficiently with a new M-PSO algorithm, outperforming existing schemes.

Findings

Significant improvement in cache hit ratio with the proposed method

Effective handling of complex heterogeneous caching scenarios

Validation through numerical analysis and simulations

Abstract

Edge caching is a new paradigm that has been exploited over the past several years to reduce the load for the core network and to enhance the content delivery performance. Many existing caching solutions only consider homogeneous caching placement due to the immense complexity associated with the heterogeneous caching models. Unlike these legacy modeling paradigms, this paper considers heterogeneous content preference of the users with heterogeneous caching models at the edge nodes. Besides, aiming to maximize the cache hit ratio (CHR) in a two-tier heterogeneous network, we let the edge nodes collaborate. However, due to complex combinatorial decision variables, the formulated problem is hard to solve in the polynomial time. Moreover, there does not even exist a ready-to-use tool or software to solve the problem. We propose a modified particle swarm optimization (M-PSO) algorithm that efficiently solves the complex constraint problem in a reasonable time. Using numerical analysis and simulation, we validate that the proposed algorithm significantly enhances the CHR performance when comparing to that of the existing baseline caching schemes.