The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning.
Explicit channel state information at the transmitter side is helpful to improve downlink precoding performance for multi-user MIMO systems.
The use of deep learning-based techniques for approximating secure encoding functions has attracted considerable interest in wireless communications due to impressive results obtained for general coding and decoding tasks for wireless communication systems.
By contrast, the present approachestablishes a robust distributed model-predictive controlscheme, in which the local subsystem controllers oper-ate under the assumption of a variable communicationschedule that is predicted by a network controller.
Systems and Control Systems and Control
However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks.
To design such a blind predictor, we use the random spectral representation of a stationary Gaussian process.