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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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 66; no. 20; pp. 5438 - 5453
Main Authors: Sun, Haoran, Chen, Xiangyi, Shi, Qingjiang, Hong, Mingyi, Fu, Xiao, Sidiropoulos, Nicholas D.
Format: Journal Article
Language:English
Published: New York IEEE 15.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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, optimization algorithms often entail considerable complexity, which creates a serious gap between theoretical design/analysis and real-time processing. In this paper, we aim at providing a new learning-based perspective to address this challenging issue. The key idea is to treat the input and output of an SP algorithm as an unknown nonlinear mapping and use a deep neural network (DNN) to approximate it. If the nonlinear mapping can be learned accurately by a DNN of moderate size, then SP tasks can be performed effectively-since passing the input through a DNN only requires a small number of simple operations. In our paper, we first identify a class of optimization algorithms that can be accurately approximated by a fully connected DNN. Second, to demonstrate the effectiveness of the proposed approach, we apply it to approximate a popular interference management algorithm, namely, the WMMSE algorithm. Extensive experiments using both synthetically generated wireless channel data and real DSL channel data have been conducted. It is shown that, in practice, only a small network is sufficient to obtain high approximation accuracy, and DNNs can achieve orders of magnitude speedup in computational time compared to the state-of-the-art interference management algorithm.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2018.2866382