The convergence of communication and computation, along with the integration
of machine learning and artificial intelligence, stand as key empowering
pillars for the sixth-generation of communication systems (6G). This paper
considers a network of one base station serving a number of devices
simultaneously using spatial multiplexing. The paper then presents an
innovative deep learning-based approach to simultaneously manage the transmit
and computing powers, alongside computation allocation, amidst uncertainties in
both channel and computing states information. More specifically, the paper
aims at proposing a robust solution that minimizes the worst-case delay across
the served devices subject to computation and power constraints. The paper uses
a deep neural network (DNN)-based solution that maps estimated channels and
computation requirements to optimized resource allocations. During training,
uncertainty samples are injected after the DNN output to jointly account for
both communication and computation estimation errors. The DNN is then trained
via backpropagation using the robust utility, thus implicitly learning the
uncertainty distributions. Our results validate the enhanced robust delay
performance of the joint uncertainty injection versus the classical DNN
approach, especially in high channel and computational uncertainty regimes.

这篇论文研究了基于深度学习的方法在 6G 通信系统中同时管理传输和计算能力以及资源分配中的不确定性。通过将估计的信道和计算需求映射到优化的资源分配中，通过在 DNN 输出后注入不确定性样本来获得更好的鲁棒性和延迟性能，特别是在高信道和计算不确定性情况下。

深度学习中基于联合不确定性注入的鲁棒通信与计算

Robust Communication and Computation using Deep Learning via Joint  Uncertainty Injection

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.

该论文提出了一种不确定性注入范式，用于培训深度学习模型以解决鲁棒优化问题，该模型能够隐含地考虑不确定性并产生统计上健壮的解决方案，而无线通信是存在不确定性的应用领域之一，在多用户 MIMO 下行传输的强鲁棒功率负载和设备到设备网络的强鲁棒功率控制中证明了所提出的训练方案的有效性。