Regression model for handover control parameter configuration in LTE-A networks. (November 2018)

• Record Type: Journal Article
• Authors: A, Saraswathi Priyadharshini; P.T.V, Bhuvaneswari
• Is Part Of: Computers & electrical engineering. Volume 72(2018)
• Page Start: 877
• Page End: 893
• Publication Date: 2018-11
• Subjects: LTE -- A3event -- Handover control parameters -- Inter-site-distance -- Regression model; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.01.011
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In long term evolution - advanced (LTE-A) networks, user assisted and network controlled hard handover (HO) procedure is adopted. In the initial phase of the HO procedure, the transmission of measurement report (MR) is initiated only when the A3 event occurs and persists for the duration of time-to-trigger (TTT). An A3 event is a triggering event which occurs when the signal strength of target evolved NodeB (eNodeB) becomes an offset better than the serving eNodeB. The Hysteresis Margin (HM) and A3Offset (A3Off) are the offset parameters involved in A3 event. Configuration of these parameters plays vital role in HO performance improvement. Most of the configuration methods of these parameters in the literature rely on either optimization rules which is rather complicated for a network of a stochastic nature or on the dynamic characteristics which is not appropriate for the long-term stability of the network. Hence, in this research the cumulative effect of HO control parameters (HCP), such as HM, A3Off and TTT, with respect to inter-site-distance and angle of user equipment movement has been analyzed. From the analysis outcome, a regression based prediction model for HCP configuration has been developed. The presented model outperforms the methods in the literature in terms of serving eNodeB coverage region and HO success region.