Proactive Caching in D2D Assisted Multitier Cellular Network

Sensors (Basel). 2022 Jul 6;22(14):5078. doi: 10.3390/s22145078.

Abstract

Cache-enabled networks suffer hugely from the challenge of content caching and content delivery. In this regard, cache-enabled device-to-device (D2D) assisted multitier cellular networks are expected to relieve the network data pressure and effectively solve the problem of content placement and content delivery. Consequently, the user can have a better opportunity to get their favored contents from nearby cache-enabled transmitters (CETs) through reliable and good-quality links; however, as expected, designing an effective caching policy is a challenging task due to the limited cache memory of CETs and uncertainty in user preferences. In this article, we introduce a joint content placement and content delivery technique for D2D assisted multitier cellular networks (D2DMCN). A support vector machine (SVM) is employed to predict the content popularity to determine which content is to be cached and where it is to be cached, thereby increasing the overall cache hit ratio (CHR). The content request is satisfied either by the neighboring node through the D2D link or by the cache-enabled base stations (BSs) of the multitier cellular networks (MCNs). Similarly, to solve the problem of optimal content delivery, the Hungarian algorithm is employed aiming to improve the quality of satisfaction. The simulation results indicate that the proposed content placement strategy effectively optimizes the overall cache hit ratio of the system. Similarly, an effective content delivery approach reduces the request content delivery delay and power consumption.

Keywords: 5G; cache-enabled D2D transmitter; content caching; content delivery; multitier cellular networks.

MeSH terms

  • Algorithms*
  • Computer Simulation
  • Feeding Behavior*

Grants and funding

This research was funded by the Research Chair of New Emerging Technologies and 5G Networks and Beyond, Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.