电子科技大学学报2020，Vol.49Issue(6)：826-836,11.DOI:10.12178/1001-0548.2020356
基于图形表示的异构超密集网络的机器学习技术研究
Machine Learning for Heterogeneous Ultra-Dense Networks with Graphical Representations
摘要
Abstract
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.关键词
深度学习/图形表式/异构超密集网络/机器学习Key words
deep learning/graphical representations/heterogeneous ultra-dense network/machine learning分类
信息技术与安全科学引用本文复制引用
樊聪敏,张颖珺,袁晓军,李思贤..基于图形表示的异构超密集网络的机器学习技术研究[J].电子科技大学学报,2020,49(6):826-836,11.基金项目
广东省重点领域研发计划(2018B010114001) （2018B010114001）
