An intelligent energy efficient handover mechanism with adaptive discontinuous reception in next generation telecommunication networks

Abstract

Long Term Evolution (LTE) and LTE-Advanced (A) networks have been introduced to accommodate the User Equipment (UEs) that supports high complex technologies such as OFDM, SC-FDMA, MIMO. Recently, 3GPP presented 5G technology to meet the performance requirement of the International Mobile Telecommunication (IMT)-2020. It supports three specific services like Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), massive Machine Type Communication (mMTC). To increase the capacity of the network, 5G will deploy an ultra-dense network in the metro area. It fulfils the spectral efficiency requirement but at the same time increases handover rate. Due to handover, the power consumption of UEs may increase dramatically. To overcome this issue, LTE-A network adopts Discontinuous Reception (DRX) to conserve battery life of UE. 5G networks will also apply evolved variations of this scheme. In general, DRX is composed of some parameters and these need to be optimized during mobility. This paper presents a method for preservation of the battery life of UEs during mobility. The proposed mechanism comprises of three parts. In the first part, Part 1, the operation of normal DRX during mobility is explored. The next parts, Part 2 and Part 3, utilize the machine learning-based methods for the pre-prediction of Target eNB (TeNB) and provide ranks to the UEs during handover. Further, DRX parameters are adjusted according to the dynamic behaviour of the network. Handover latency, end-to-end delay is improved by 20% and 55%, respectively as compared to the conventional scheme. Further, machine learning based (Part 2) and dynamic adjustment of DRX parameters (Part 3) contribute to enhance the power saving factor by maximum up to 85%, and 33% as compared to state of art mechanism.