SINR Prediction in Mobile CDMA Systems by Linear and Nonlinear Artificial Neural-Network-Based Predictors
This article describes linear and nonlinear Artificial Neural
Network(ANN)-based predictors as Autoregressive Moving
Average models with Auxiliary input (ARMAX) process for
Signal to Interference plus Noise Ratio (SINR) prediction in
Direct Sequence Code Division Multiple Access (DS/CDMA)
systems. The Multi Layer Perceptron (MLP) neural network
with nonlinear function is used as nonlinear neural network and
Adaptive Linear (Adaline) predictor is used as linear predictor.
The problem of complexity of the MLP and Adaline structures
is solved by using the Minimum Mean Squared Error (MMSE)
principle to select the optimal numbers of input and hidden
nodes by try and error role. Simulation results show that both
of MLP and Adaline optimal neural networks can track the
effect of deep fading due to using a 1.8 GHZ carrier frequency
at the urban mobile speeds of 10 km/h, 50 km/h and 120 km/h
with tolerable estimation errors. Therefore, the neural networkbased
predictor is well suitable SINR-based predictor in closedloop
power control to combat multi path fading in CDMA
systems.
Keywords: Neural Networks, DS/CDMA, Multi Path Fading Channel, Closed-Loop Mobile Power Control, SINR Prediction, Neural Network Optimization
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