NDLI: 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

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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

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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

Content Provider	IET Digital Library
Author	Lotfollahzadeh, Tahereh Kabiri, Sepideh Kalbkhani, Hashem Shayesteh, Mahrokh G.
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.
Starting Page	1
Ending Page	11
Page Count	11
ISSN	17519675
Volume Number	10
e-ISSN	17519683
Issue Number	Issue 1, Feb (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/10/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2014.0265
Journal	IET Signal Processing
Publisher Date	2016-02-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Autoregressive Processes Centralised Control Centralised Power Control Algorithm Evolution Computational Complexity Computational Complexity Reduction FBS Clustering Femtocell Base Station Clustering Femtocellular Radio Mobile Radio System Nonlinear Logistic Smooth Transition Autoregressive-based Predicted Signal-to-interference-plus-noise Ratio Outage Probability Improvement Pattern Clustering Power Control Probability Radio Frequency Interference Reliability SINR Statistics Telecommunication Network Reliability Telecommunication Power Management Two-tier Femtocell Network Two-tier Macrocell Network Wireless Network
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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