Femtocell base station clustering and logistic smooth transition autoregressive‐based predicted signal‐to‐interference‐plus‐noise ratio for performance improvement of two‐tier macro/femtocell networks. Issue 1 (1st February 2016)

• Record Type: Journal Article
• Authors: Lotfollahzadeh, Tahereh; Kabiri, Sepideh; Kalbkhani, Hashem; Shayesteh, Mahrokh G.
• Is Part Of: IET signal processing. Volume 10:Issue 1(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-02-01
• Subjects: femtocellular radio -- pattern clustering -- autoregressive processes -- radiofrequency interference -- power control -- telecommunication power management -- centralised control -- computational complexity -- probability -- telecommunication network reliability; femtocell base station clustering -- nonlinear logistic smooth transition autoregressive‐based predicted signal‐to‐interference‐plus‐noise ratio -- two‐tier macrocell network -- wireless networks -- centralised power control algorithm evolution -- FBS clustering -- SINR -- computational complexity reduction -- outage probability improvement -- two‐tier femtocell network; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2014.0265
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : The aim of this study is to improve the performance of two‐tier macro/femtocell networks using a power control approach. In wireless networks, power control plays an important role in improving a number of performance parameters such as co‐channel interference and outage probability reduction, throughput increasing, and power saving. This study explores the evolution of centralised power control algorithm based on femtocell base station (FBS) clustering and predicted signal‐to‐interference‐plus‐noise ratio (SINR) of users. To reduce the computational complexity of centralised algorithm, dense deployed femtocells are considered in different clusters. In this case, femtocells inside one cluster make considerable interference to each other, while the interferences from femtocells of other clusters are negligible. Moreover, because of the non‐linearity of SINR samples, non‐linear logistic smooth transition autoregressive (LSTAR) model is used to model the SINR data, and then the next SINR samples are predicted from the previous samples. According to the clustered FBSs and predicted SINR, the proposed power control scheme is applied to femtocell network in the downlink. The results demonstrate that the introduced method improves the outage probability and throughput and outperforms previous methods significantly.