Nahid Ardalani and Ahmadreza Khoogar and H. Roohi A Comparison of Adaline and MLP Neural Network based Predictors in SIR Estimation in Mobile DSCDMA Systems
1390 - 1395
2007
1
9
International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering http://waset.org/publications/5727
http://waset.org/publications/9
World Academy of Science, Engineering and Technology
In this paper we compare the response of linear and
nonlinear neural networkbased prediction schemes in prediction of
received SignaltoInterference Power Ratio (SIR) in Direct
Sequence Code Division Multiple Access (DSCDMA) systems. The
nonlinear predictor is Multilayer Perceptron MLP and the linear
predictor is an Adaptive Linear (Adaline) predictor. We solve the
problem of complexity by using the Minimum Mean Squared Error
(MMSE) principle to select the optimal predictors. The optimized
Adaline predictor is compared to optimized MLP by employing
noisy Rayleigh fading signals with 1.8 GHZ carrier frequency in an
urban environment. The results show that the Adaline predictor can
estimates SIR with the same error as MLP when the user has the
velocity of 5 kmh and 60 kmh but by increasing the velocity upto
120 kmh the mean squared error of MLP is two times more than
Adaline predictor. This makes the Adaline predictor (with lower
complexity) more suitable than MLP for closedloop power control
where efficient and accurate identification of the timevarying
inverse dynamics of the multi path fading channel is required.
International Science Index 9, 2007