Title
Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks.
Abstract
Both caching and interference alignment (IA) are promising techniques for next-generation wireless networks. Nevertheless, most of the existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the cha...
Year
DOI
Venue
2017
10.1109/TVT.2017.2751641
IEEE Transactions on Vehicular Technology
Keywords
Field
DocType
Learning (artificial intelligence),Transmitters,Wireless networks,Interference,Receivers,Time-varying channels
Wireless network,Wireless,Cache,Computer science,Efficient energy use,Computer network,Communication channel,Interference (wave propagation),Invariant (mathematics),Reinforcement learning
Journal
Volume
Issue
ISSN
66
11
0018-9545
Citations 
PageRank 
References 
33
0.94
26
Authors
7
Name
Order
Citations
PageRank
Ying He124812.27
Zheng Zhang226729.51
Fei Yu35116335.58
Nan Zhao41591123.85
Hongxi Yin524219.35
Victor C. M. Leung69717759.02
Yanhua Zhang714524.84