Yazar "Seyman, M. Nuri" seçeneğine göre listele

Listeleniyor 1 - 5 / 5
  • Küçük Resim
    Öğe
    Back Propagation Neural Network Approach for Channel Estimation in OFDM System
    (Ieee, 2010) Taspinar, Necmi; Seyman, M. Nuri
    In 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.
  • [ X ]
    Öğe
    Channel estimation based on adaptive neuro-fuzzy inference system in OFDM
    (Ieice-Inst Electronics Information Communications Eng, 2008) Seyman, M. Nuri; Taspinar, Necmi
    In 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.
  • [ X ]
    Öğe
    Channel Estimation Based on Neural Network With Feedback for Mimo Ofdm Mobile Communication Systems
    (Taylor & Francis Inc, 2012) Seyman, M. Nuri; Taspinar, Necmi
    Multiple 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.
  • [ X ]
    Öğe
    Energy Detection Approach for Spectrum Sensing in Cognitive Radio Systems
    (Kırıkkale Üniversitesi, 2018) Yılmazel, Rüstem; Seyman, M. Nuri; Tuna, Eyüp
    Because a large number of licensed users haveoccasionally used their allocated bandwidth in recent years, efficient use offrequency band is a vital issue. While licensed users continue to use thisbandwidth for publishing, unused bands can be allocated for reuse to providebroadband network services. These empty bands are described as white space andcover a large part of the bandwidth. It is possible to activate this greatpotential by cognitive radio technology. In this study, energy detectiontechnique was used to detect unused bands. With this technique, it isdetermined which frequency band is suitable and there will be doneappropriately the assignment of the second user.
  • Küçük Resim
    Öğe
    Radial Basis Function Neural Networks for Channel Estimation in MIMO-OFDM Systems
    (Springer Heidelberg, 2013) Seyman, M. Nuri; Taspinar, Necmi
    Orthogonal 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.