An Intelligent Scheduling Architecture for Mixed Traffic in LTE-Advanced Networks

Abstract

In this chapter, an intelligent scheduling architecture is presented for the downlink transmission of LTE-Advanced networks to enhance the Quality of Service (QoS) provision to different traffic types while maintaining system level performance such as system throughput and fairness. Hebbian learning process and K-mean clustering algorithm are integrated in the time domain of the proposed scheduling architecture to intelligently allocate the available radio resource to Real Time (RT) and Non-Real Time (NRT) traffic types. The integration of these algorithms allows just enough resource allocation to RT traffic and diverts the remaining resource to NRT traffic to fulfil its minimum throughput requirements. System level simulation is set up for the performance evaluation, and simulation results show that the proposed scheduling architecture reduces average delay, delay violation probability, and average Packet Drop Rate (PDR) of RT traffic while guaranteeing the support of minimum throughput to NRT traffic and maintaining system throughput at good level.