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Öğe Back Propagation Neural Network Approach for Channel Estimation in OFDM System(Ieee, 2010) Taspinar, Necmi; Seyman, M. NuriIn high data rate communication systems which use orthogonal frequency division multiplexing as a modulation scheme, at receiver channel impulse responses must be estimated for coherent demodulation. In this paper, multilayered perceptrons (MLP) neural network with backpropagation (BP) learning algorithm is proposed as a channel estimator for OFDM systems. Our proposed MLP neural channel estimator is compared to least square (LS) algorithm, minimum mean square error (MMSE) algorithm and radial basis function neural network (RBF) in respect to bit error rate (BER) and mean square error (MSE) criteria in order to evaluate the performances. MLP neural network has better performance than LS algorithm and RBF neural network and its performance is close to MMSE algorithm and the perfect channel impulse responses. Moreover, there is unnecessary of channel statistics, matrix computation and noise information when our proposed neural network is used for channel estimation.Öğe Channel estimation based on adaptive neuro-fuzzy inference system in OFDM(Ieice-Inst Electronics Information Communications Eng, 2008) Seyman, M. Nuri; Taspinar, NecmiIn this letter we purpose adaptive neuro-fuzzy inference system (ANFIS) for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. To evaluate the performance of this estimator, we compare the ANFIS with least square (LS) algorithm, minimum mean square error (MMSE) algorithm by using bit error rate (BER) and mean square error (MSE) criterias. According to computer simulations the performance of ANFIS has better performance than LS algorithm and close to MMSE algorithm. Besides there is unnecessity to send pilot when used the ANFIS.Öğe Channel estimation based on neural network in space time block coded MIMO-OFDM system(Academic Press Inc Elsevier Science, 2013) Seyman, Muhammet Nuri; Taspinar, NecmiIn this study, we propose feed-forward multilayered perceptron (MLP) neural network trained with the Levenberg-Marquardt algorithm to estimate channel parameters in MIMO-OFDM systems. Bit error rate (BER) and mean square error (MSE) performances of least square (LS) and least mean square error (LMS) algorithms are also compared to our proposed neural network to evaluate the performances. Neural network channel estimator has got much better performance than LS and LMS algorithms. Furthermore it doesn't need channel statistics and sending pilot tones, contrary to classical algorithms. Crown Copyright (C) 2012 Published by Elsevier Inc. All rights reserved.Öğe Channel Estimation Based on Neural Network With Feedback for Mimo Ofdm Mobile Communication Systems(Taylor & Francis Inc, 2012) Seyman, M. Nuri; Taspinar, NecmiMultiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) has received a great deal of attention of recently in achieving high data rate in wireless communication systems such as WIMAX. Channel estimation is, however, a critical issue for coherent demodulation. In this paper, a new channel estimator based on neural network with feedback for MIMO-OFDM mobile system is designed and its performance is compared to the least square error (LS), least mean square error (LMS), minimum mean square error (MMSE) algorithms and neural network without feedback by using computer simulations. Simulation results demonstrate that our proposed system is an effective solution to channel estimation in time varying fast fading channels without any knowledge of channel statistics and noise information.Öğe Particle swarm optimization for pilot tones design in MIMO-OFDM systems(Springer, 2011) Seyman, Muhammet Nuri; Taspinar, NecmiChannel estimation is an essential task in MIMO-OFDM systems for coherent demodulation and data detection. Also designing pilot tones that affect the channel estimation performance is an important issue for these systems. For this reason, in this article we propose particle swarm optimization (PSO) to optimize placement and power of the comb-type pilot tones that are used for least square (LS) channel estimation in MIMO-OFDM systems. To optimize the pilot tones, upper bound of MSE is used as the objective function of PSO. The effects of Doppler shifts on designing pilot tones are also investigated. According to the simulation results, PSO is an effective solution for designing pilot tones.Öğe Pilot Tones Optimization Using Artificial Bee Colony Algorithm for MIMO-OFDM Systems(Springer, 2013) Seyman, Muhammet Nuri; Taspinar, NecmiHow to design the pilot tones that are used in channel estimation has a significant effect on the estimation performance. To achieve good performance in least square (LS) algorithm, we propose the artificial bee colony (ABC) algorithm for optimizing the placement of pilot tones in MIMO-OFDM systems. We also derive the upper bound of mean square error of LS estimation with the help of Gerschgorin disc theorem for fitness function of ABC algorithm. The results show that designing pilot tones using the ABC algorithm outperforms other considered placement strategies in terms of high system performance and low computational complexity.Öğe Radial Basis Function Neural Networks for Channel Estimation in MIMO-OFDM Systems(Springer Heidelberg, 2013) Seyman, M. Nuri; Taspinar, NecmiOrthogonal frequency division multiplexing (OFDM) combined with multiple input multiple output (MIMO) antennas is one of the promising schemes for high rate data transmission and capacity improvement. However, in these systems, channel estimation task is critical for coherent detection and demodulation. In this study, we have proposed a channel estimator based on radial basis function neural network trained by gradient descent method for MIMO-OFDM systems. Simulation results show that the proposed estimator performs better than other considered channel estimation techniques.
