Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 Introduction
- Part I Optimal Cache Placement and Delivery
- Part II Proactive Caching
- 7 Learning Popularity for Proactive Caching in Cellular Networks
- 8 Wireless Edge Caching for Mobile Social Networks
- 9 A Proactive and Big DataEnabled Caching Analysis Perspective
- 10 Mobility-Aware Caching in Cellular Networks
- Part III Cache-Aided Interference and Physical Layer Management
- Part IV Energy-Efficiency, Security, Economic, and Deployment
- Index
7 - Learning Popularity for Proactive Caching in Cellular Networks
from Part II - Proactive Caching
Published online by Cambridge University Press: 19 October 2020
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 Introduction
- Part I Optimal Cache Placement and Delivery
- Part II Proactive Caching
- 7 Learning Popularity for Proactive Caching in Cellular Networks
- 8 Wireless Edge Caching for Mobile Social Networks
- 9 A Proactive and Big DataEnabled Caching Analysis Perspective
- 10 Mobility-Aware Caching in Cellular Networks
- Part III Cache-Aided Interference and Physical Layer Management
- Part IV Energy-Efficiency, Security, Economic, and Deployment
- Index
Summary
Video data have been showed to dominate a significant portion of mobile data traffic and have a strong influence on a backhaul congestion issue in cellular networks. To tackle the problem, proactive caching is considered as a prominent candidate in terms of cost efficiency. In this chapter, we study a novel popularity-predicting-based caching procedure that takes raw video data as input to determine an optimal cache placement policy, which deals with both published and unpublished videos. For dealing with unpublished videos whose statistical information is unknown, features from the video content are extracted and condensed into a high-dimensional vector. This type of vector is then mapped to a lower-dimensional space. This process not only alleviates the computational burden but also creates a new vector that is more meaningful and comprehensive. At this stage, different types of prediction models can be trained to anticipate the popularity, for which information from published videos is used as training data.
- Type
- Chapter
- Information
- Wireless Edge CachingModeling, Analysis, and Optimization, pp. 127 - 145Publisher: Cambridge University PressPrint publication year: 2021