Volume 16, Number 3

Intelligent Cognitive Engine for 5G Network Quality of Service Management

  Authors

Ifeanyi Stanly Nwokoro¹, Muhammad Qaim Aliyu Sambo², Ifeanyi Friday Eze³, Sanusi Yusuf Ahmed⁴, Timothy Ola Akinfenwa⁵, Zacciah Kwaku Adom-Oduro⁶, Osakpamwan George Oshodin⁷, Julius Tunji Okesola⁸, Nwatu Augustina Nebechi⁹, ¹Rhema University, Nigeria, ²Five Stars ICT Ltd, Nigeria, ³First Bank, Nigeria, ⁴Bank of Industry, Nigeria, ⁵Osun State University, Nigeria, ⁶University of Professional Studies Accra, Ghana, ⁷Addbeams Nigeria Ltd, Nigeria⁸First Tech University, Nigeria, ⁹Alex Ekwueme Federal University Ndufu-Alike Ebonyi State, Nigeria

  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.