Abstract
We first briefly discuss the operating principle of the temporal difference (TD) method. A TD method-based multi-step ahead prediction scheme using the modified Elman neural network (MENN) is then set up. This prediction approach provides for online adaptation and fast convergence rate. Next, it is applied to the prediction of the occurrence of long term deep fading in mobile communication systems. Simulation experiments reveal that our prediction scheme is capable of predicting the degree of occurrence possibility of deep fading. Based on this prediction result, the power control of cellular phone systems employing the reinforcement learning method will be investigated in the future.
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