Modelling and forecasting of signal‐to‐interference plus noise ratio in femtocellular networks using logistic smooth threshold autoregressive model. Issue 1 (1st February 2015)

• Record Type: Journal Article
• Authors: Kabiri, Sepideh; Lotfollahzadeh, Tahereh; Shayesteh, Mahrokh G.; Kalbkhani, Hashem
• Is Part Of: IET signal processing. Volume 9:Issue 1(2015)
• Page Start: 48
• Page End: 59
• Publication Date: 2015-02-01
• Subjects: femtocellular radio -- autoregressive processes -- interference suppression -- forecasting theory -- least mean squares methods -- nonlinear estimation -- statistical testing; signal‐to‐interference plus noise ratio -- nonlinear statistical model -- two tier heterogeneous cellular networks -- femtocell base stations -- SINR forecasting -- received signal strength -- AR modelling -- mean square error method -- noise effects -- interference effects -- macro‐femto cellular network -- F‐test -- nonlinear logistic smooth threshold autoregressive model -- LSTAR model -- Kolmogorov–Smirnov test; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2014.0065
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : The aim of this paper is to present a non‐linear statistical model to fit and forecast the signal‐to‐interference plus noise ratio (SINR) in two‐tier heterogeneous cellular networks which consist of macrocells and femtocells. Since in these networks the number and locations of femtocell base stations (FBS) are variable, SINR forecasting can be useful in some areas such as power control and handover management. So far, linear autoregressive (AR) models have commonly been used in forecasting the received signal strength (rss) in macrocellular networks. However, AR modelling results in high mean square error (MSE) when data are non‐linear. This paper focuses on SINR which takes into account signal strength, interference and noise effects. Moreover, macro‐femto cellular network is considered. The F ‐test results show that the SINR data are non‐linear, leading to use non‐linear models instead of AR model. A non‐linear logistic smooth threshold AR (LSTAR) model is utilised to model and forecast the SINR data. Kolmogorov–Smirnov (K‐S) test demonstrates that LSTAR provides good fitness to the SINR samples. The results indicate that LSTAR model achieves much better performance in modelling and forecasting of SINR data than the AR model.