Handover optimization scheme for LTE-Advance networks based on AHP-TOPSIS and Q-learning

With the emblematical expansion of telecommunication networks, high data rate, and low latency for User Equipment (UE) has arisen. 3GPP introduced Long Term Evolution (LTE), LTE-Advance (A) in release 8 and release 10, respectively. These technologies support different types of traffic in a network such as a video, voice, data, etc. It also creates new challenges for resource management and seamless connectivity when UEs are mobile. Handover facilitates transfer the control of UE from serving evolve Node (eNB) to target eNB without any interruption. During mobility of UEs, if the triggering time and selection of eNB out of available eNBs is not done optimally for handover, then Quality of Service (QoS) requirements may be breached. Hence, for seamless connectivity, selection of eNB and triggering points needs to be further optimized to satisfy QoS requirements. In this paper, an intelligent scheme based on AHP-TOPSIS method and Q-learning approach is proposed for handover optimization. The performance of the network with the proposed scheme is analyzed numerically and with the help of simulation also. Results show that the proposed scheme minimizes the Handover Failure Rate (HFR) and Handover Ping-Pong (HPP), effectively to 28%, 25% and 35%, 33% as compared to conventional method and Fuzzy Multiple-Criteria Cell Selection (FMCCS) scheme.