The fast evolution of the number of wireless users and the emergence of new multimedia services have motivated third-generation partnership project (3GPP) to develop new radio access technologies. Thus, the carrier aggregation (CA) was introduced from version 10 long-term evolution (LTE), known as long-term evolution-advanced (LTE-A), to meet the increasing demands in terms of throughput and bandwidth and to ensure the Quality of Service (QoS) for different classes of bearers in LTE networks. However, such solution stills inefficient until implementing good resources management scheme. Several scheduling mechanisms have been proposed in the literature, to guarantee the QoS of different classes of bearers in LTE-A and 5G networks. Nevertheless, most of them promote high-priority bearers. In this study, a new approach of uplink scheduling resources has been developed. It aims to ensure service fairness of different traffic classes that allocates bearers over LTE-A and 5G networks. Also, it raises the number of admitted users in the network by increasing the number of admitted bearers through a dynamic management of service priorities. In fact, the low-priority traffic classes, using low-priority bearers, are favoured during a specific time interval, based on the average waiting time for each class. Simulation results show that the QoS parameters were much improved for the low-priority classes without significantly affecting the QoS of high priority ones.
Machine-Learning Assisted Optimisation of Free-Parameters of a Dual-Input Power Amplifier for Wideband Applications
