Machine learning techniques applied to call admission control in 5G mobile networks

Highly reliable applications with low latency are a key feature in 5G networks. In the prevailing scenario of efficient mobile network systems, the Quality-of-Service (QoS) depends on the regulation of traffic volume in wireless communications, known as the Call Admission Control (CAC). 5G networks are also very important for Intelligent Transportation System (ITS) as they can be used for quick detection and controlling of traffic hence can be informative, sustainable and more effective. Machine learning is the concept of providing the power to learn and develop mechanically, by practising. It also provides the power to attain learning and developing in absence of classical methods such as programming. It also permits wireless networks such as 5G to be increasingly dynamic and predictive. With this feature, the formulation of 5G vision seems possible. With the use of machine learning and neural networks, this paper proposes various CAC methods deployed for 5G multimedia mobile networks. This can be achieved by delivering the best attributes of soft computing which are deployed in the current mobile networks for ensuring recovery of efficiency of the prevailing CAC methods.