Şenol, Habib
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Şenol, Habib
H.,Şenol
H. Şenol
Habib, Şenol
Senol, Habib
H.,Senol
H. Senol
Habib, Senol
Senol, H.
Şenol,H.
Senol, H
Şenol, Habib
H.,Şenol
H. Şenol
Habib, Şenol
Senol, Habib
H.,Senol
H. Senol
Habib, Senol
Senol, H.
Şenol,H.
Senol, H
Şenol, Habib
Job Title
Doç. Dr.
Email Address
Main Affiliation
Computer Engineering
Computer Engineering
05. Faculty of Engineering and Natural Sciences
01. Kadir Has University
Computer Engineering
05. Faculty of Engineering and Natural Sciences
01. Kadir Has University
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
15
LIFE ON LAND
0
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
2
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
4
QUALITY EDUCATION
0
Research Products
2
ZERO HUNGER
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
1
NO POVERTY
0
Research Products
5
GENDER EQUALITY
0
Research Products
14
LIFE BELOW WATER
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
13
CLIMATE ACTION
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
3
GOOD HEALTH AND WELL-BEING
1
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
49
Articles
21
Views / Downloads
23/0
Supervised MSc Theses
3
Supervised PhD Theses
0
WoS Citation Count
345
Scopus Citation Count
454
WoS h-index
9
Scopus h-index
11
Patents
0
Projects
0
WoS Citations per Publication
7.04
Scopus Citations per Publication
9.27
Open Access Source
33
Supervised Theses
3
| Journal | Count |
|---|---|
| IEEE Transactions on Signal Processing | 7 |
| IEEE Transactions on Wireless Communications | 3 |
| Wireless Personal Communications | 2 |
| 2008 IEEE International Conference on Acoustics, Speech and Signal Processing | 1 |
| 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications | 1 |
Current Page: 1 / 6
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49 results
Scholarly Output Search Results
Now showing 1 - 10 of 49
Conference Object Citation - WoS: 3Pilot-Aided Bayesian Mmse Channel Estimation for Ofdm Systems(Ieee, 2004) Senol, H; Çirpan, HA; Panayirci, EThis paper proposes a computationally efficient, pilot-aided minimum mean square error (MMSE) channel estimation algorithm for OFDM systems. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and estimates uncorrelated series expansion coefficients. Moreover, optimal rank reduction is achieved in the proposed approach 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 first consider the stochastic Cramer-Rao hound and derive the closed-form expression for the random KL coefficients. We then exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE.Conference Object Mp-Sage Based Channel Estimation for Underwater Cooperative Ofdm Systems(IEEE, 2013) Erdoğan, Mustafa; Şenol, Habib; Panayırcı, Erdal; Uysal, MuratIn this paper an efficient channel estimation algorithm is proposed for amplify-and-forward (AF) cooperative relay based orthogonal frequency division multiplexing (OFDM) system in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise The algorithm is based on the combinations of the matching pursuit (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 Gaussian mixture noise distribution parameters in an iterative way Computer simulations show that underwater acoustic channel is estimated very effectively and the proposed algorithm has excellent symbol error rate (SER) and channel estimation performance as compared to the existing onesConference Object Effect of Inter-Block Region on Compressed Sensing Based Channel Estimation in Tds-Ofdm Systems(IEEE, 2016) Başaran, Mehmet; Erküçük, Serhat; Şenol, Habib; Cirpan, Hakan AliTime 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.Article Citation - Scopus: 19Channel Estimation for TDS-OFDM Systems in Rapidly Time-Varying Mobile Channels(Institute of Electrical and Electronics Engineers Inc., 2018) Basaran,M.; Şenol,H.; 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.Conference Object Citation - Scopus: 1Semiblind Joint Channel Estimation and Equalization for Ofdm Systems in Rapidly Varying Channels(2010) Şenol, Habib; Panayırcı, Erdal; Poor, H. Vincent; Oğuz, Onur; Vandendorpe, LucWe describe a new joint iterative channel estimation and equalization algorithm for joint channel estimation and data detection for orthogonal frequency division multiplexing (OFDM) systems in the presence of frequency selective and rapidly timevarying channels. The algorithm is based on the expectation maximization-maximum a posteriori (EM-MAP) technique which is very suitable for the multicarrier signal formats. The algorithm leads to a receiver structure that yields the equalized output using the channel estimates. The pilot symbols are employed to estimate the initial channel coefficients effectively and unknown data symbols are averaged out in the algorithm. The band-limited discrete cosine serial expansion of low dimensionality is employed to represent the time-varying fading channel. In this way the resulting reduced dimensional channel coefficients are estimated iteratively with tractable complexity. The extensive computer simulations show that the algorithm has excellent symbol error rate (SER) and mean square error (MSE) performances for very high mobility even during the initialization step. Copyright © ?enol et. al.Conference Object Citation - WoS: 1Joint Channel Estimation and Equalization for Ofdm Based Broadband Communications in Rapidly Varying Mobile Channels(IEEE, 2010) Şenol, Habib; Poor, H. VincentThis 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.Master Thesis Capturing the Data Similarity Among Organizations of Same Nature(Kadir Has Üniversitesi, 2021) Ishaq, Waqar; Şenol, HabibThe 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 measurementArticle Citation - WoS: 4Citation - Scopus: 6Channel Estimation for Realistic Indoor Optical Wireless Communication in Aco-Ofdm Systems(Springer, 2018) Özmen, Atilla; Şenol, HabibIn 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.Article Citation - WoS: 1Citation - Scopus: 1Bayesian Estimation of Discrete-Time Cellular Neural Network Coefficients(TUBITAK Scientific & Technical Research Council Turkey, 2017) Özer, Hakan Metin; Özmen, Atilla; Şenol, HabibA 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.Article Citation - WoS: 40Citation - Scopus: 51Sparse Channel Estimation and Equalization for Ofdm-Based Underwater Cooperative Systemsw With Amplify-And Relaying(IEEE-INST Electrical Electronics Engineers Inc, 2016) Panayırcı, Erdal; Şenol, Habib; Uysal, Murat; Poor, H. VincentThis paper is concerned with a challenging problem of channel estimation and equalization for amplify-and-forward cooperative relay based orthogonal frequency division multi-plexing (OFDM) systems in sparse underwater acoustic (UWA) channels. The sparseness of the channel impulse response and prior information for the non-Gaussian channel gains modeled by an exact continuous Gaussian mixture (CGM) are exploited to improve the performance of the channel estimation algorithm. The resulting novel algorithm initially estimates the overall sparse complex-valued channel taps from the source to the destination as well as their locations using the matching pursuit (MP) approach. The effective time-domain non-Gaussian noise is approximated well as a Gaussian noise in the frequency-domain where the estimation takes place. An efficient and low complexity algorithm is developed based on a combination of the MP and the maximum a posteriori probability (MAP) based space-alternating generalized expectation-maximization technique to improve the estimates of the channel taps and their locations in an iterative manner. Computer simulations show that the UWA channel is estimated very effectively and the proposed algorithm exhibits excellent symbol error rate and channel estimation performance.
