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.
翻译:本文提出了为培训深层次学习模式注入不确定性以解决稳健优化问题的范例。关于深层次学习的现有研究大多侧重于模型学习能力,同时假定投入数据的质量和准确性。然而,在现实应用深层次学习解决优化问题的实际应用中,投入的准确性(这是本案中存在问题的参数)起着很大的作用。这是因为,在许多情况下,准确获得问题参数往往费用高昂或有时不可能,因此,非常有必要制定学习算法,说明投入的不确定性,并针对这些不确定性提出强有力的解决办法。本文件为培训机器学习模型提出了一个新的不确定性注入计划,能够隐含地说明不确定性并产生具有统计上稳健的解决办法。我们进一步确定无线通信是一个应用领域,在诸如频道系数等问题参数中普遍存在不确定性。我们显示了拟议的培训计划在两种应用中的有效性:对多用户的多次投入-多输出(MIIMO)下链传输的强大电荷负荷;以及对设备到设计(D2D)网络的强大电源控制。</s>