Open Science Research Excellence
@article{(International Science Index):,
  title    = {A Comparison of Adaline and MLP Neural Network based Predictors in SIR Estimation in Mobile DS/CDMA Systems},
  author    = {Nahid Ardalani and  Ahmadreza Khoogar and  H. Roohi},
  country   = {},
  abstract  = {In this paper we compare the response of linear and
nonlinear neural network-based prediction schemes in prediction of
received Signal-to-Interference Power Ratio (SIR) in Direct
Sequence Code Division Multiple Access (DS/CDMA) 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 km/h and 60 km/h but by increasing the velocity up-to
120 km/h 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 closed-loop power control
where efficient and accurate identification of the time-varying
inverse dynamics of the multi path fading channel is required.},
    journal   = {International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering},  volume    = {1},
  number    = {9},
  year      = {2007},
  pages     = {1390 - 1395},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 9, 2007},