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
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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
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
14
LIFE BELOW WATER
0
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
3
GOOD HEALTH AND WELL-BEING
1
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
4
QUALITY EDUCATION
0
Research Products
2
ZERO HUNGER
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
2
Research Products
13
CLIMATE ACTION
0
Research Products
1
NO POVERTY
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
5
GENDER EQUALITY
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Scholarly Output
48
Articles
20
Views / Downloads
268/5773
Supervised MSc Theses
3
Supervised PhD Theses
0
WoS Citation Count
345
Scopus Citation Count
451
WoS h-index
9
Scopus h-index
11
Patents
0
Projects
0
WoS Citations per Publication
7.19
Scopus Citations per Publication
9.40
Open Access Source
32
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 |
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48 results
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Now showing 1 - 10 of 48
Conference Object Citation - WoS: 1Citation - Scopus: 2Data-Aided Autoregressive Sparse Channel Tracking for Ofdm Systems(IEEE, 2016) Buyuksar, Ayse Betul; Şenol, Habib; Erküçük, Serhat; Cirpan, Hakan AliIn 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.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: 14Citation - Scopus: 17Joint Channel Estimation and Symbol Detection for Ofdm Systems in Rapidly Time-Varying Sparse Multipath Channels(Springer, 2015) Şenol, HabibIn 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.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.Article Citation - WoS: 4Citation - Scopus: 6A Low-Complexity Time-Domain Mmse Channel Estimator for Space-time/Frequency Block-Coded Ofdm Systems(Hindawi Publishing Corporation, 2006) Şenol, Habib; Çırpan, Hakan Ali; Panayırcı, ErdalFocusing on transmit diversity orthogonal frequency-division multiplexing (OFDM) transmission through frequency-selective channels this paper pursues a channel estimation approach in time domain for both space-frequency OFDM (SF-OFDM) and space-time OFDM (ST-OFDM) systems based on AR channel modelling. The paper proposes a computationally efficient pilot-aided linear minimum mean-square-error (MMSE) time-domain channel estimation algorithm for OFDM systems with transmitter diversity in unknown wireless fading channels. The proposed approach employs a convenient representation of the channel impulse responses based on the Karhunen-Loeve (KL) orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion no matrix inversion is required in the proposed MMSE estimator. Subsequently optimal rank reduction is applied to obtain significant taps resulting in a smaller computational load on the proposed estimation algorithm. The performance of the proposed approach is studied through the analytical results and computer simulations. In order to explore the performance the closed-form expression for the average symbol error rate (SER) probability is derived for the maximum ratio receive combiner (MRRC). We then consider the stochastic Cramer-Rao lower bound(CRLB) and derive the closed-form expression for the random KL coefficients and consequently exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also analyze the effect of a modelling mismatch on the estimator performance. Simulation results confirm our theoretical analysis and illustrate that the proposed algorithms are capable of tracking fast fading and improving overall performance. Copyright (C) 2006 Hindawi Publishing Corporation. All rights reserved.Article Citation - WoS: 7Citation - Scopus: 10Artificial Neural Network Based Estimation of Sparse Multipath Channels in Ofdm Systems(SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS, 2021) Şenol, Habib; Abdur Rehman Bin, Tahir; Özmen, AtillaIn 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.Conference Object Frequency selective fading channel estimation in OFDM systems using KL expansion(2005) Şenol, Habib; Cirpan, Hakan Ali; Panayırcı, ErdalThis 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.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 ones
