A Delay-Aware Caching Algorithm for Wireless D2D Caching Networks

TL;DR

This paper introduces an efficient delay-aware caching algorithm for D2D-enabled wireless networks, significantly reducing transmission delay and improving throughput by optimizing file placement among users.

Contribution

The paper proposes a novel, low-complexity caching algorithm that optimizes <file,user> pairs for minimal delay, adaptable to changing network conditions in 5G systems.

Findings

The proposed algorithm outperforms naive caching strategies in delay reduction.

Numerical results confirm the algorithm's effectiveness in dynamic scenarios.

The method achieves high throughput with low computational complexity.

Abstract

Recently, wireless caching techniques have been studied to satisfy lower delay requirements and offload traffic from peak periods. By storing parts of the popular files at the mobile users, users can locate some of their requested files in their own caches or the caches at their neighbors. In the latter case, when a user receives files from its neighbors, device-to-device (D2D) communication is enabled. D2D communication underlaid with cellular networks is also a new paradigm for the upcoming 5G wireless systems. By allowing a pair of adjacent D2D users to communicate directly, D2D communication can achieve higher throughput, better energy efficiency and lower traffic delay. In this work, we propose a very efficient caching algorithm for D2D-enabled cellular networks to minimize the average transmission delay. Instead of searching over all possible solutions, our algorithm finds out the best <file,user> pairs, which provide the best delay improvement in each loop to form a caching policy with very low transmission delay and high throughput. This algorithm is also extended to address a more general scenario, in which the distributions of fading coefficients and values of system parameters potentially change over time. Via numerical results, the superiority of the proposed algorithm is verified by comparing it with a naive algorithm, in which all users simply cache their favorite files.