Learning to Optimize: Training Deep Neural Networks for Interference Management

Numerical optimization has played a central role in addressing key signal processing (SP) problems. Highly effective methods have been developed for a large variety of SP applications such as communications, radar, filter design, and speech and image analytics, just to name a few. However, optimizat...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 66; H. 20; S. 5438 - 5453
Hauptverfasser:	Sun, Haoran, Chen, Xiangyi, Shi, Qingjiang, Hong, Mingyi, Fu, Xiao, Sidiropoulos, Nicholas D.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 15.10.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation algorithms Artificial neural networks Computing time deep neural networks Design analysis Filter design (mathematics) Interference interference management Machine learning algorithms Management Mapping Neural networks Optimization Optimization algorithms Optimization algorithms approximation Radar imaging Signal processing Signal processing algorithms Task analysis Wireless communication WMMSE algorithm
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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