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Uncertainty Injection: A Deep Learning Method for Robust Optimization
- Source :
- IEEE Transactions on Wireless Communications. :1-1
- Publication Year :
- 2023
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2023.
-
Abstract
- This paper proposes a paradigm of uncertainty injection for training deep learning model to solve robust optimization problems. The majority of existing studies on deep learning focus on the model learning capability, while assuming the quality and accuracy of the inputs data can be guaranteed. However, in realistic applications of deep learning for solving optimization problems, the accuracy of inputs, which are the problem parameters in this case, plays a large role. This is because, in many situations, it is often costly or sometime impossible to obtain the problem parameters accurately, and correspondingly, it is highly desirable to develop learning algorithms that can account for the uncertainties in the input and produce solutions that are robust against these uncertainties. This paper presents a novel uncertainty injection scheme for training machine learning models that are capable of implicitly accounting for the uncertainties and producing statistically robust solutions. We further identify the wireless communications as an application field where uncertainties are prevalent in problem parameters such as the channel coefficients. We show the effectiveness of the proposed training scheme in two applications: the robust power loading for multiuser multiple-input-multiple-output (MIMO) downlink transmissions; and the robust power control for device-to-device (D2D) networks.<br />13 pages, 7 figures. To appear in IEEE Transactions on Wireless Communications
- Subjects :
- Networking and Internet Architecture (cs.NI)
Signal Processing (eess.SP)
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Artificial Intelligence
Applied Mathematics
Machine Learning (stat.ML)
Machine Learning (cs.LG)
Computer Science Applications
Computer Science - Networking and Internet Architecture
Artificial Intelligence (cs.AI)
Statistics - Machine Learning
FOS: Electrical engineering, electronic engineering, information engineering
Electrical Engineering and Systems Science - Signal Processing
Electrical and Electronic Engineering
Subjects
Details
- ISSN :
- 15582248 and 15361276
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Wireless Communications
- Accession number :
- edsair.doi.dedup.....e478352202028fc6434cf8d0a0aba818