Fine-grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks

TL;DR

This paper presents a novel spatio-temporal-social multi-feature framework with edge-enhanced graph convolution and LSTM for fine-grained prediction of content delivery hotspots in 5G ultra-dense networks, improving accuracy significantly.

Contribution

It introduces a new encoding, sampling, and feature extraction framework with a graph convolution block and LSTM for accurate CDSs hotspot prediction in 5G UDNs.

Findings

Improves prediction AUC by 40.5% over state-of-the-art methods.

Achieves 80% prediction coverage of unknown areas.

Operates at a fine-grained spatial granularity of 76 meters.

Abstract

The 5G networks have extensively promoted the growth of mobile users and novel applications, and with the skyrocketing user requests for a large amount of popular content, the consequent content delivery services (CDSs) have been bringing a heavy load to mobile service providers. As a key mission in intelligent networks management, understanding and predicting the distribution of CDSs benefits many tasks of modern network services such as resource provisioning and proactive content caching for content delivery networks. However, the revolutions in novel ubiquitous network architectures led by ultra-dense networks (UDNs) make the task extremely challenging. Specifically, conventional methods face the challenges of insufficient spatio precision, lacking generalizability, and complex multi-feature dependencies of user requests, making their effectiveness unreliable in CDSs prediction under 5G UDNs. In this paper, we propose to adopt a series of encoding and sampling methods to model CDSs of known and unknown areas at a tailored fine-grained level. Moreover, we design a spatio-temporal-social multi-feature extraction framework for CDSs hotspots prediction, in which a novel edge-enhanced graph convolution block is proposed to encode dynamic CDSs networks based on the social relationships and the spatio features. Besides, we introduce the Long-Short Term Memory (LSTM) to further capture the temporal dependency. Extensive performance evaluations with real-world measurement data collected in two mobile content applications demonstrate the effectiveness of our proposed solution, which can improve the prediction area under the curve (AUC) by 40.5% compared to the state-of-the-art proposals at a spatio granularity of 76m, with up to 80% of the unknown areas.