Browsing by Author "Şenol, Habib"

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Citation - WoS: 7
Citation - Scopus: 10
Artificial Neural Network Based Estimation of Sparse Multipath Channels in Ofdm Systems
(SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS, 2021-01) Şenol, Habib; Şenol, Habib; Abdur Rehman Bin, Tahir; Özmen, Atilla; Özmen, Atilla
In order to increase the transceiver performance in frequency selective fading channel environment, orthogonal frequency division multiplexing (OFDM) system is used to combat inter-symbol-interference. In this work, a channel estimation scheme for an OFDM system in the presence of sparse multipath channel is studied using the artificial neural networks (ANN). By means of ANN's learning capability, it is shown that how to model and obtain a channel estimate and how it allows the proposed technique to give a better system throughput. The performance of proposed method is compared with the Matching Pursuit (MP) and Orthogonal MP (OMP) algorithms that are commonly used in compressed sensing literature in order to estimate delay locations and tap coefficients of a sparse multipath channel. In this work, we propose a performance- efficient ANN based sparse channel estimator with lower computational cost than that of MP and OMP based channel estimators. Even though there is a slight performance lost in a few simulation scenarios in which we have lower computational complexity advantage, in most scenarios, our computer simulations corroborate that our low complexity ANN based channel estimator has better mean squared error and the corresponding symbol error rate performances comparing with MP and OMP algorithms.
Artificial Neural Network Based Sparse Channel Estimation for Ofdm Systems
(Kadir Has Üniversitesi, 2017) Tahir, Abdur Rehman Bin; Özmen, Atilla; Şenol, Habib; Özmen, Atilla
In order to increase the communication quality in frequency selective fading channel environment, orthogonal frequency division multiplexing (OFDM) systems are used to combat inter-symbol-interference (ISI). In this thesis, a channel estimation scheme for the OFDM system in the presence of sparse multipath channel is studied. The channel estimation is done by using the artificial neural networks (ANNs) with Resilient Backpropagation training algorithm. This technique uses the learning capability of artificial neural networks. By means of this feature we show how to obtain a channel estimate and how it allows the proposed technique to be less computationally complex; as there is no need for any matrix inversions. This proposed method is compared with the Matching Pursuit (MP) algorithm that is well known estimation technique for sparse channels. The results show that the ANN based channel estimate is computationally simpler and a small number of pilots are required to get a better estimate of the channel especially in low SNR levels. With this setting, the proposed algorithm leads to a better system throughput.
Citation - WoS: 1
Citation - Scopus: 1
Bayesian Estimation of Discrete-Time Cellular Neural Network Coefficients
(TUBITAK Scientific & Technical Research Council Turkey, 2017) Özer, Hakan Metin; Şenol, Habib; Özmen, Atilla; Özmen, Atilla; Şenol, Habib
A new method for finding the network coefficients of a discrete-time cellular neural network (DTCNN) is proposed. This new method uses a probabilistic approach that itself uses Bayesian learning to estimate the network coefficients. A posterior probability density function (PDF) is composed using the likelihood and prior PDFs derived from the system model and prior information respectively. This posterior PDF is used to draw samples with the help of the Metropolis algorithm a special case of the Metropolis--Hastings algorithm where the proposal distribution function is symmetric and resulting samples are then averaged to find the minimum mean square error (MMSE) estimate of the network coefficients. A couple of image processing applications are performed using these estimated parameters and the results are compared with those of some well-known methods.
Capturing the Data Similarity Among Organizations of Same Nature
(Kadir Has Üniversitesi, 2021) Ishaq, Waqar; Şenol, Habib; Şenol, Habib
The vertical collaborative clustering aims to unravel the hidden structure of data (similarity) among different sites, which will help data owners to make a smart decision without sharing actual data. For example, various hospitals located in different regions want to investigate the structure of common disease among people of different populations to identify latent causes without sharing actual data with other hospitals. Similarly, a chain of regional educational institutions wants to evaluate their students' performance belonging to different regions based on common latent constructs. The available methods used for finding hidden structures are complicated and biased to perform collaboration in measuring similarity among multiple sites. In this dissertation, the author proposed two approaches of vertical collaborative clustering, namely (1) Vertical Collaborative Clustering Model (2) Vertical Collaborative Clustering based on Bit-Plane Slicing, with superior accuracy over the state of the art approaches. The Vertical Collaborative Clustering Model (V CCM) manages the collaboration among multiple data sites using Self-Organizing Map (SOM). It includes standard procedure and tuning of the exchanged information in specific proportionality to augment the learning process of the clustering via collaboration. Moreover, the VCCM unravels hidden information without compromising the data confidentiality. The aim of the model is to set an ideal environment for the collaboration process among multiple sites. The VCCM is evaluated by purity measurement, using four datasets (Iris, Geyser, Cancer and Waveform). The findings of this study show the significance of the VCCM by comparing the collaborative results with the local results using purity measurement. The VCCM unlocks possible reasons determining impact of collaboration based on related and unrelated patterns. The results demonstrate that the proposed VCCM improves local learning by collaboration and also helps the data owner to make better decisions on the clustering. Additionally, the results obtained have better accuracy than the existing approaches. The proposed Vertical Collaborative Clustering based on Bit-Plane Slicing (VCCBPS) is simple and unique approach with improved accuracy, manages collaboration among various data sites. The VCC-BPS transforms data from input space to code space, capturing maximum similarity locally and collaboratively at a particular bit plane. The findings of this study highlight the significance of those particular bits which fit the model in correctly classifying clusters locally and collaboratively. Thenceforth, the data owner appraises local and collaborative results to reach a better decision. The VCC-BPS is validated by Geyser, Skin and Iris datasets and its results are compared with the composite dataset. It is found that the VCCBPS outperforms existing solutions with improved accuracy in term of purity and Davies-Bouldin index to manage collaboration among different data sites. It also performs data compression by representing a large number of observations with a small number of data symbols. Keywords: Collaborative clustering, Collaboration, Vertical collaborative clustering, Cluster combination, Purity measurement, Similarity measurement
Citation - WoS: 4
Citation - Scopus: 6
Channel Estimation for Realistic Indoor Optical Wireless Communication in Aco-Ofdm Systems
(Springer, 2018) Özmen, Atilla; Özmen, Atilla; Şenol, Habib; Şenol, Habib
In this paper channel estimation problem in a visible light communication system is considered. The information data is transmitted using asymmetrical clipped optical orthogonal frequency division multiplexing. Channel estimation and symbol detection are performed by the Maximum Likelihood and the Linear Minimum Mean Square Error detection techniques respectively. The system performance is investigated in realistic environment that is simulated using an indoor channel model. Two different channels are produced using the indoor channel model. Symbol error rate (SER) performance of the system with estimated channels is presented for QPSK and 16-QAM digital modulation types and compared with the perfect channel state information. As a mean square error (MSE) performance benchmark for the channel estimator Cramer-Rao lower bound is also derived. MSE and SER performances of the simulation results are presented.
Citation - WoS: 35
Citation - Scopus: 41
Channel Estimation for Residual Self-Interference in Full-Duplex Amplify-And Two-Way Relays
(IEEE-INST Electrical Electronics Engineers Inc, 2017) Li, Xiaofeng; Şenol, Habib; Tepedelenlioglu, Cihan; Şenol, Habib
Training schemes for full duplex two-way relays are investigated. We propose a novel one-block training scheme with a maximum likelihood estimator to estimate the channels between the nodes as well as the residual self-interference (RSI) channel simultaneously. A quasi-Newton algorithm is used to solve the estimator. As a baseline a multi-block training scheme is also considered. The Cramer-Rao bounds of the one-block and multi-block training schemes are derived. By using the Szego's theorem about Toeplitz matrices we analyze how the channel parameters and transmit powers affect the Fisher information. We show analytically that exploiting the structure arising from the RSI channel increases its Fisher information. Numerical results show the benefits of estimating the RSI channel.
Citation - Scopus: 18
Channel Estimation for TDS-OFDM Systems in Rapidly Time-Varying Mobile Channels
(Institute of Electrical and Electronics Engineers Inc., 2018) Şenol, Habib; Erküçük, Serhat; Erküçük,S.; Çirpan,H.A.
This paper explores the performance of time-domain synchronous orthogonal frequency-division multiplexing (TDS-OFDM) systems operated under rapidly time-varying mobile channels. Since a rapidly time-varying channel contains more unknown channel coefficients than the number of observations, the mobile channel can conveniently be modeled with the discrete Legendre polynomial basis expansion model to reduce the number of unknowns. The linear minimum mean square error (LMMSE) estimate can be exploited for channel estimation on inter-block-interference-free received signal samples owing to transmitting pseudo-noise (PN) sequences. In conventional TDS-OFDM systems, the channel estimation performance is limited due to estimating channel responses only from the beginning part of the channel. Therefore, a new system model named "partitioned TDS-OFDM system" is proposed to improve the system performance by inserting multiple PN sequences to the middle and end parts of the channel as well. In addition to providing the reconstruction error performance, Bayesian Cramer-Rao lower bound is derived analytically. Also, the LMMSE-based symbol detection is employed. To alleviate the negative effects of inter-carrier-interference (ICI) occuring in mobile channels, ICI cancellation is applied to enhance the detection performance. The simulation results demonstrate that the proposed TDS-OFDM system is superior to the conventional system and its corresponding performance is able to approach the achievable lower performance bound. © 2018 IEEE.
Citation - WoS: 13
Citation - Scopus: 18
Channel Estimation for Tds-Ofdm Systems in Rapidly Time-Varying Mobile Channels
(IEEE-Inst Electrical Electronics Engineers Inc, 2018) Başaran, Mehmet; Şenol, Habib; Şenol, Habib; Erküçük, Serhat; Erküçük, Serhat; Çırpan, Hakan Ali
This paper explores the performance of time-domain synchronous orthogonal frequency-division multiplexing (TDS-OFDM) systems operated under rapidly time-varying mobile channels. Since a rapidly time-varying channel contains more unknown channel coefficients than the number of observations, the mobile channel can conveniently be modeled with the discrete Legendre polynomial basis expansion model to reduce the number of unknowns. The linear minimum mean square error (LMMSE) estimate can be exploited for channel estimation on inter-block-interference-free received signal samples owing to transmitting pseudo-noise (PN) sequences. In conventional TDS-OFDM systems, the channel estimation performance is limited due to estimating channel responses only from the beginning part of the channel. Therefore, a new system model named "partitioned TDS-OFDM system" is proposed to improve the system performance by inserting multiple PN sequences to the middle and end parts of the channel as well. In addition to providing the reconstruction error performance, Bayesian Cramer-Rao lower hound is derived analytically. Also, the LMMSE-based symbol detection is employed. To alleviate the negative effects of inter-carrier-interference (ICI) occuring in mobile channels, ICI cancellation is applied to enhance the detection performance. The simulation results demonstrate that the proposed TDS-OFDM system is superior to the conventional system and its corresponding performance is able to approach the achievable lower performance bound.
Citation - WoS: 1
Citation - Scopus: 4
Channel Estimation in Underwater Cooperative Ofdm System With Amplify-And Relaying
(IEEE, 2012) Şenol, Habib; Şenol, Habib; Panayırcı, Erdal; Panayırcı, Erdal; Erdoğan, Mustafa; Uysal, Murat
This paper is concerned with a challenging problem of channel estimation for amplify-and-forward cooperative relay based orthogonal frequency division multiplexing (OFDM) systems in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise. We exploit the sparse structure of the channel impulse response to improve the performance of the channel estimation algorithm due to the reduced number of taps to be estimated. The resulting novel algorithm initially estimates the overall sparse channel taps from the source to the destination as well as their locations using the matching pursuit (MP) approach. The correlated non-Gaussian effective noise is modeled as a Gaussian mixture. Based on the Gaussian mixture model an efficient and low complexity algorithm is developed based on the combinations of the MP and the space-alternating generalized expectation-maximization (SAGE) technique to improve the estimates of the channel taps and their location as well as the noise distribution parameters in an iterative way. The proposed SAGE algorithm is designed in such a way that by choosing the admissible hidden data properly on which the SAGE algorithm relies a subset of parameters is updated for analytical tractability and the remaining parameters for faster convergence Computer simulations show that underwater acoustic (UWA) channel is estimated very effectively and the proposed algorithm has excellent symbol error rate and channel estimation performance.
Citation - WoS: 1
Citation - Scopus: 2
Data-Aided Autoregressive Sparse Channel Tracking for Ofdm Systems
(IEEE, 2016) Buyuksar, Ayse Betul; Şenol, Habib; Şenol, Habib; Erküçük, Serhat; Erküçük, Serhat; Cirpan, Hakan Ali
In order to meet future communication system requirements channel estimation over fast fading and frequency selective channels is crucial. In this paper Space Alternated Generalized Expectation Maximization Maximum a Posteriori (SAGE-MAP) based channel estimation algorithm is proposed for Orthogonal Frequency Division Multiplexing (OFDM) systems for Autoregressive (AR) modeled time-varying sparse channels. Also an initialization algorithm has been developed from the widely used sparse approximation algorithm Orthogonal Matching Pursuit (OMP) since the performance of SAGE algorithm strictly depends on initialization. The results show that multipath delay positions can be tracked successfully for every time instant using the proposed SAGE-MAP based approach.
Citation - WoS: 1
Citation - Scopus: 4
Distributed Estimation Over Parallel Fading Channels With Channel Estimation Error
(IEEE, 2008) Şenol, Habib; Şenol, Habib; Tepedelenlioğlu, Cihan
We consider distributed estimation of a source observed by sensors in additive Gaussian noise where the sensors are connected to a fusion center with unknown orthogonal (parallel) flat Rayleigh fading channels. We adopt a two-phase approach of (i) channel estimation with training and (ii) source estimation given the channel estimates where the total power is fixed. We prove that allocating half the total power into training is optimal and show that compared to the perfect channel case a performance loss of at least 6 dB is incurred. In addition we show that unlike the perfect channel case increasing the number of sensors will lead to an eventual degradation in performance. We characterize the optimum number of sensors as a function of the total power and noise statistics. Simulations corroborate our analytical findings.
Citation - WoS: 1
Citation - Scopus: 3
Distributed Estimation With Channel Estimation Error Over Orthogonal Fading Channels
(IEEE, 2007) Şenol, Habib; Şenol, Habib; Tepedelenlioğlu, Cihan
We study distributed estimation of a source corrupted by an additive Gaussian noise and observed by sensors which are connected to a fusion center with unknown orthogonal (parallel) flat Rayleigh fading channels. The fading communication channels are estimated with training. Subsequently source estimation given the channel estimates and transmitted sensor observations is performed. We consider a setting where the estimated channels are fed-back to the sensors for optimal power allocation which leads to a threshold behavior of sensors with bad channels being unused (inactive). We also show that at least half of the total power should be used for training. Simulation results corroborate our analytical findings.
Citation - WoS: 0
Effect of Inter-Block Region on Compressed Sensing Based Channel Estimation in Tds-Ofdm Systems
(IEEE, 2016) Başaran, Mehmet; Erküçük, Serhat; Erküçük, Serhat; Şenol, Habib; Şenol, Habib; Cirpan, Hakan Ali
Time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) is the basis technology for digital television standard (DTV) employed in some countries thanks to its high spectral efficiency when compared to traditional cyclic prefix OFDM. Moreover it does not require pilot usage in frequency domain channel estimation. Instead of data usage as cyclic prefix pseudo-noise (PN) sequences are transmitted in guard intervals. Due to interference from the previous OFDM data symbol the received signal in guard interval can be decomposed into a small-sized signal that contains only PN sequences utilizing the inter-block-interference (IBI)-free region in the convolution matrix. Due to sparsity multipath fading channel can be obtained by the application of compressed sensing (CS) technique to reconstruct the high-dimensional sparse channel from the decreased-size of received signal through the known PN sequence matrix. In this study the effect of the size of IBI-free region on CS and Bayesian CS (BCS) based channel estimation is investigated. Accordingly reconstruction error performances of basis pursuit (BP) and BCS are compared. Simulation results show that the channel estimation can be improved by trading-off the length of the IBI-free region. However an increase in IBI-free region leads to decreased energy efficiency at both the transmitter and receiver side.
Citation - Scopus: 1
Effect of Inter-Block Region on Compressed Sensing Based Channel Estimation in Tds-Ofdm Systems
(Institute of Electrical and Electronics Engineers Inc., 2016) Başaran, Mehmet; Erküçük, Serhat; Erküçük, Serhat; Şenol, Habib; Şenol, Habib; Çırpan, Hakan Ali
Time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) is the basis technology for digital television standard (DTV) employed in some countries thanks to its high spectral efficiency when compared to traditional cyclic prefix OFDM. Moreover, it does not require pilot usage in frequency domain channel estimation. Instead of data usage as cyclic prefix, pseudo-noise (PN) sequences are transmitted in guard intervals. Due to interference from the previous OFDM data symbol, the received signal in guard interval can be decomposed into a small-sized signal that contains only PN sequences utilizing the inter-block-interference (IBI)-free region in the convolution matrix. Due to sparsity, multipath fading channel can be obtained by the application of compressed sensing (CS) technique to reconstruct the high-dimensional sparse channel from the decreased-size of received signal through the known PN sequence matrix. In this study, the effect of the size of IBI-free region on CS and Bayesian CS (BCS) based channel estimation is investigated. Accordingly, reconstruction error performances of basis pursuit (BP) and BCS are compared. Simulation results show that the channel estimation can be improved by trading-off the length of the IBI-free region. However, an increase in IBI-free region leads to decreased energy efficiency at both the transmitter and receiver side.
Citation - WoS: 0
Effect of the Channel Estimation Error on the Performance of the Source Estimator in a Wireless Sensor Network With Orthogonal Channels
(IEEE, 2008) Şenol, Habib; Şenol, Habib; Tepedelenlioğlu, Cihan
In this work effect of the channel estimation error on the MSE performance of the source estimator in a wireless sensor network with orthogonal flat fading channels is studied. A two-phase approach was employed where in the first phase the orthogonal fading channel coefficients are estimated and in the second phase channel estimates and sensor observations transmitted to fusion center are used for the source estimation. We consider a sensor network in which the channel estimates are fed-back to the sensors for optimal power allocation which leads to switch off the sensors with bad channels in the second phase. We also show that training power should be at least half of the total power. Our analytical findings are corroborated by simulation results.
Citation - Scopus: 0
Effect of the Channel Estimation Error on the Performance of the Source Estimator in a Wireless Sensor Network With Orthogonal Channels;
(2008) Şenol,H.; Şenol, Habib; Tepedelenlioǧlu,C.
In this work, effect of the channel estimation error on the MSE performance of the source estimator in a wireless sensor network with orthogonal flat fading channels is studied. A two-phase approach was employed where in the first phase, the orthogonal fading channel coefficients are estimated, and in the second phase, channel estimates and sensor observations transmitted to fusion center are used for the source estimation. We consider a sensor network in which the channel estimates are fed-back to the sensors for optimal power allocation which leads to switch off the sensors with bad channels in the second phase. We also show that training power should be at least half of the total power. Our analytical findings are corroborated by simulation results. ©2008 IEEE.
Citation - Scopus: 0
Frequency selective fading channel estimation in OFDM systems using KL expansion
(2005) Şenol, Habib; Şenol, Habib; Cirpan, Hakan Ali; Panayırcı, Erdal; Panayırcı, Erdal
This paper proposes a computationally efficient linear minimum mean square error (MMSE) channel estimation algorithm based on KL series expansion for OFDM systems. Based on such expansion no matrix inversion is required in the proposed MMSE estimator. Moreover truncation in the linear expansion of channel is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We provide performance analysis results studying the influence of the effect of SNR and correlation mismatch on the estimator performance. Simulation results confirm our theoretical results and illustrate that the proposed algorithm is capable of tracking fast fading and improving performance.
Citation - WoS: 5
Citation - Scopus: 5
Information Theoretical Performance Limits of Single-Carrier Underwater Acoustic Systems
(Inst Engineering Technology-IET, 2014) Nouri, Hatef; Şenol, Habib; Uysal, Murat; Panayırcı, Erdal; Panayırcı, Erdal; Şenol, Habib
In this study the authors investigate the information theoretical limits on the performance of point-to-point single-carrier acoustic systems over frequency-selective underwater channels with intersymbol interference. Under the assumptions of sparse and frequency-selective Rician fading channel and non-white correlated Gaussian ambient noise the authors derive an expression for channel capacity and demonstrate the dependency on channel parameters such as the number location and power delay profile of significant taps as well as environmental parameters such as distance temperature salinity pressure and depth. Then the authors use this expression to determine the optimal carrier frequency input signalling and bandwidth for capacity maximisation.
Citation - WoS: 1
Citation - Scopus: 0
Joint Channel Estimation and Equalization for Ofdm Based Broadband Communications in Rapidly Varying Mobile Channels
(IEEE, 2010) Şenol, Habib; Şenol, Habib; Poor, H. Vincent
This paper is concerned with the challenging and timely problem of channel estimation for orthogonal frequency division multiplexing (OFDM) systems in the presence of frequency selective and very rapidly time varying channels. In OFDM systems operating over rapidly time-varying channels the orthogonality between subcarriers is destroyed leading to inter-carrier interference (ICI) and resulting in an irreducible error floor. The band-limited discrete cosine serial expansion of low-dimensionality is employed to represent the time-varying channel. In this way the resulting reduced dimensional channel coefficients are estimated iteratively with tractable complexity and independently of the channel statistics. The algorithm is based on the expectation maximization-maximum a posteriori probability (EM-MAP) technique leading to a receiver structure that also yields the equalized output using the channel estimates. The pilot symbols are employed to estimate the initial coefficients effectively and unknown data symbols are averaged out in the algorithm in a non-data-aided fashion. It is shown that the computational complexity of the proposed algorithm to estimate the channel coefficients and to generate the equalized output as a by-product is similar to O(N) per detected symbol N being the number of OFDM subcarriers. Computational complexity as well as computer simulations carried out for the systems described in WiMAX and LTE standards indicate that it has significant performance and complexity advantages over existing suboptimal channel estimation and equalization algorithms proposed earlier in the literature.
Citation - WoS: 14
Citation - Scopus: 17
Joint Channel Estimation and Symbol Detection for Ofdm Systems in Rapidly Time-Varying Sparse Multipath Channels
(Springer, 2015) Şenol, Habib; Şenol, Habib
In this paper we propose a space-alternating generalized expectation maximization (SAGE) based joint channel estimation and data detection algorithm in compressive sensing (CS) framework for orthogonal frequency-division multiplexing (OFDM) systems in rapidly time-varying sparse multipath channels. Using dynamic parametric channel model the sparse multipath channel is parameterized by a small number of distinct paths each represented by the path delays and path gains. In our model we assume that the path gains rapidly vary within the OFDM symbol duration while the number of paths and path delays vary symbol by symbol. Since the convergency of the SAGE algorithm needs statistically independent parameter set of interest to be estimated we specifically choose the discrete orthonormal Karhunen-Loeve basis expansion model (DKL-BEM) to provide statistically independent BEM coefficients within one OFDM symbol duration and use just a few significant BEM coefficients to represent the rapidly time-varying path gains. The resulting SAGE algorithm that also incorporates inter-channel interference cancellation updates the data sequences and the channel parameters serially. The computer simulations show that our proposed algorithm has better channel estimation and symbol error rate performance than that of the orthogonal matching pursuit algorithm that is commonly proposed in the CS literature.