Massive MIMO systems are considered as one the leading enabler of 5G wireless communication. In this technology, the transmitter and receiver are equipped with very large number of antennas. This can potentially allow for higher data rates and better spectral efficiency.
One of the main challenges is massive MIMO system is the hardware complexity.
When considering hundreds of antennas, dedicated RF chain per antenna like in traditional MIMO systems is no longer possible. Hence, it is desirable to reduce the number of RF chains is the system while still benefiting from the large number of antennas.
To this aim, a hybrid analog-digital architecture is suggested, where some of the processing, traditionally performed in the digital domain, are shifted to the analog domain. This technique is call hybrid analog-digital beamforming
Therefore, an efficient design method for the hybrid beamformer is required.
The goal of the project is to develop a deep learning framework for hybrid beamformers design: to define a cost function, produce a learning set of efficient beamformers, and develop the deep algorithm to produce such beamformers given a new system setting.
The project will include research next to matlab implementation