Education & Resources

This webinar will discuss the MMSE channel estimator for a simple SIMO system model, without knowledge of the required channel statistics. Although the derived MMSE estimator is computationally intractable in the general form, its structure can be used to motivate a neural network architecture with lower complexity.

Graphs are generic models of signal structure that can help to learn in several practical problems. To learn from graph data, we need scalable architectures that can be trained on moderate dataset sizes and that can be implemented in a distributed manner. Drawing from graph signal processing, the webinar will define graph convolutions and use them to introduce graph neural networks (GNNs). 

Signal sampling and reconstruction is a fundamental engineering task at the heart of signal processing. The celebrated Shannon-Nyquist theorem guarantees perfect signal reconstruction from uniform samples, obtained at a rate twice the maximum frequency present in the signal. 

The potentials of using millimeter-wave (mmWave) frequency for future wireless cellular communication systems have motivated the study of large-scale antenna arrays for achieving highly directional beamforming. However, the conventional fully digital beamforming methods, which require one radio frequency (RF) chain per antenna element, are not viable for large-scale antenna arrays due to the high cost and high power consumption of RF chain components in high frequencies.