Uncertainty injection: A deep learning method for robust optimization
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 …
solve robust optimization problems. The majority of existing studies on deep learning focus …
Deep learning for robust power control for wireless networks
Robust optimization is an important task in wireless communications, because due to fading
and feedback delay there is inherent uncertainty in channel state information in a wireless …
and feedback delay there is inherent uncertainty in channel state information in a wireless …
Data-driven robust optimization using deep neural networks
Robust optimization has been established as a leading methodology to approach decision
problems under uncertainty. To derive a robust optimization model, a central ingredient is to …
problems under uncertainty. To derive a robust optimization model, a central ingredient is to …
Data-driven robust optimization using unsupervised deep learning
Robust optimization has been established as a leading methodology to approach decision
problems under uncertainty. To derive a robust optimization model, a central ingredient is to …
problems under uncertainty. To derive a robust optimization model, a central ingredient is to …
Learning for robust combinatorial optimization: Algorithm and application
Learning to optimize (L2O) has recently emerged as a promising approach to solving
optimization problems by exploiting the strong prediction power of neural networks and …
optimization problems by exploiting the strong prediction power of neural networks and …
Learning to continuously optimize wireless resource in a dynamic environment: A bilevel optimization perspective
There has been a growing interest in developing data-driven, and in particular deep neural
network (DNN) based methods for modern communication tasks. These methods achieve …
network (DNN) based methods for modern communication tasks. These methods achieve …
Unsupervised deep learning for optimizing wireless systems with instantaneous and statistic constraints
Deep neural networks (DNNs) have been introduced for designing wireless policies by
approximating the mappings from environmental parameters to solutions of optimization …
approximating the mappings from environmental parameters to solutions of optimization …
Constrained deep learning for wireless resource management
In this paper, we investigate a deep learning (DL) approach to solve a generic constrained
optimization problem in wireless networks, where the objective and constraint functions can …
optimization problem in wireless networks, where the objective and constraint functions can …
Learning-based robust resource allocation for D2D underlaying cellular network
In this paper, we study the resource allocation in D2D underlaying cellular network with
uncertain channel state information (CSI). For satisfying the minimum rate requirement for …
uncertain channel state information (CSI). For satisfying the minimum rate requirement for …
Robust optimization in machine learning
Learning, optimization, and decision making from data must cope with uncertainty
introduced both implicitly and explicitly. Uncertainty can be explicitly introduced when the …
introduced both implicitly and explicitly. Uncertainty can be explicitly introduced when the …