  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.
