Heterogeneous ultra-dense network (H-UDN) is envisioned as a promising solution to sustain the explosive mobile traffic demand through network densification. By placing access points, processors, and storage units as close as possible to mobile users, H-UDNs bring forth a number of advantages, including high spectral efficiency, high energy efficiency, and low latency. Nonetheless, the high density and diversity of network entities in H-UDNs introduce formidable design challenges in collaborative signal processing and resource management. This article illustrates the great potential of machine learning techniques in solving these challenges. In particular, we show how to utilize graphical representations of H-UDNs to design efficient machine learning algorithms.
@article{arxiv.1808.04547,
title = {Machine Learning for Heterogeneous Ultra-Dense Networks with Graphical Representations},
author = {Congmin Fan and Ying-Jun Angela Zhang and Xiaojun Yuan},
journal= {arXiv preprint arXiv:1808.04547},
year = {2018}
}