The back propagation algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a training algorithm consisting of a learning rate and a momentum factor. The major drawbacks of above learning algorithm are the problems of local minima and slow convergence speeds. The addition of an extra term, called a proportional factor reduces the convergence of the back propagation algorithm. We have applied the three term back propagation to multiplicative neural network learning. The algorithm is tested on XOR and parity problem and compared with the standard back propagation training algorithm.
The performance of an OFDM system can be strongly degraded by the presence of random phase noise especially if a system design targets high data rates at high carrier frequencies. Phase noise affects the OFDM system by constellation rotation i.e. the common phase error (CPE) and causes inter carrier interference (ICI). In this paper we propose an algorithm for phase noise reduction using an extended Kalman filter (EKF) and compare its performance with maximum likelihood estimation (MLE).
Simulation results for QAM and PSK show the effectiveness of the presented algorithm.Keywords--OFDM, phase noise, extended kalman filter (EKF).
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