Machine Learning And 5G – Stepping Up The Cellular Base Stations Game

5G is the main component of a new type of “genius” network to help with the advanced levels of complexity that they require.

Prediction and real-time decision making are incredibly intense processes that require advanced hardware and software solutions, which are only possible with 5G tech.

Details like the capacity to be more dynamic with real-time network technology like resource loading, interference detection, and power budget-balancing make networks “smart” in the 4G era.

However, with 5G, there is support for new antenna capabilities, high-density and heterogenous network topologies, downlink and uplink channel configuration and allocation based on application and payload type.

Machine learning has many advantages across all layers of a 5G network, from the physical layer all through the application layer.

Moore Resources And Extra Coordination

One of the telltale signs of 5G technology is advanced antenna capabilities, which include massive multiple-input multiple-output (MIMO) antenna arrays, beam steering, and beamforming.

Extended MIMO is the use of antenna arrays with a high number of active elements.

Regarding the frequency band in which it is used, large MIMO systems can rely on collections of antennas between 24 and several hundred units.

MIMO And Machine Learning

One of the purposes of MIMO, in general, is the ability to transmit parallel and redundant data streams to address errors provoked by interference of sorts.

MIMO is also useful in beamforming and beam steering, an advanced system.

Beamforming is the capacity of using a set of phased arrays to create a beam of energy that can focus and boost signal transmission and reception to and from a base station to a specific mobile device.

Machine learning is used at the base station to return real-time and predictive analysis and modeling to better coordinate, configure, schedule, and adjust which arrays to come in play and at what time.