access icon free Modelling and forecasting of signal-to-interference plus noise ratio in femtocellular networks using logistic smooth threshold autoregressive model

© The Institution of Engineering and Technology
Received 12/02/2014, Accepted 23/06/2014, Revised 10/06/2014, Published 24/02/2015

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.

Inspec keywords: statistical testing; autoregressive processes; nonlinear estimation; least mean squares methods; interference suppression; forecasting theory; femtocellular radio

Other keywords: nonlinear statistical model; LSTAR model; noise effects; mean square error method; nonlinear logistic smooth threshold autoregressive model; received signal strength; F-test; AR modelling; femtocell base stations; Kolmogorov–Smirnov test; two tier heterogeneous cellular networks; macro-femto cellular network; interference effects; SINR forecasting; signal-to-interference plus noise ratio

Subjects: Interpolation and function approximation (numerical analysis); Signal processing and detection; Mobile radio systems; Other topics in statistics; Electromagnetic compatibility and interference

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