Improved Estimation of Sir in Mobile Cdma Systems by Integration of Artificial Neural Network and Time Series Technique

This study presents an integrated Artificial Neural Network (ANN) and time series framework to estimate and predict Signal to Interference Ratio (SIR) in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. It is difficult to model uncertain behavior of SIR with only conventional ANN or time series and the integrated algorithm could be an ideal substitute for such cases. Artificial Neural Network (ANN) approach based on supervised multi layer perceptron (MLP) network are used in the proposed algorithm. All type of ANN-MLP are examined in present study. At last, Coefficient of Determination (R ) is used for selecting preferred model from different 2 constructed MLP-ANN. One of unique feature of the proposed algorithm is utilization of Autocorrelation Function (ACF) to define input variables whereas conventional methods which use trial and error method. This is the first study that integrates ANN and time series for improved estimation of SIR in mobile CDMA systems.

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