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

In this paper, we present a framework for resource allocations for multicast
device-to-device (D2D) communications underlaying a cellular network. The
objective is to maximize the sum throughput of active cellular users (CUs) and
feasible D2D groups in a cell, while meeting a certain
signal-to-interferenceplus- noise ratio (SINR) constraint for both the CUs and
D2D groups. We formulate the problem of power and channel allocation as a mixed
integer nonlinear programming (MINLP) problem where one D2D group can reuse the
channels of multiple CUs and the channel of each CU can be reused by multiple
D2D groups. Distinct from existing approaches in the literature, our
formulation and solution methods provide an effective and flexible means to
utilize radio resources in cellular networks and share them with multicast
groups without causing harmful interference to each other. A variant of the
generalized bender decomposition (GBD) is applied to optimally solve the MINLP
problem. A greedy algorithm and a low-complexity heuristic solution are then
devised. The performance of all schemes is evaluated through extensive
simulations. Numerical results demonstrate that the proposed greedy algorithm
can achieve closeto- optimal performance, and the heuristic algorithm provides
good performance, though inferior than that of the greedy, with much lower
complexity.

本文介绍了一个用于蜂窝网络下的组播设备对设备通信资源分配的框架，主要通过混合整数非线性规划来解决干扰问题和优化资源利用效率，提供了贪心算法和启发式算法来优化研究结果。