Implementing Machine Learning Techniques for Artificial Neural Networks in 5G Network Quality of Service Management

ABSTRACT:

5G-New scenario transparency in communication between various types of networks that are interconnected is expected in radio multimedia. A prevalent issue across all these diverse platforms is the equitable distribution of the restricted network resource among rival apps. A transcendent measure of how equitably properties are distributed to end-users is called Quality of Service (QoS). It is derived from subscriber satisfaction levels and depends on how quickly the network responds to possible infractions of established regulatory guidelines. There are discussion on the perspective of 5G network trust in terms of QoS management, demand formulation, xMBB, M-MTC, and U-MTC. There is a proposed architecture that controls access in the 5G network's data plane. A new cognitive engine for artificial intelligence that is built on memory is put forth. The idea is to translate the probabilistic sign of a set of variables related to resource distribution to the end-user for multi-service improvement.

Keywords: Deep Reinforcement Learning, Memory-Based Artificial Intelligence, 5G Quality of Service, Machine Learning.