Browsing by Author "Cirpan, Hakan Ali"

Now showing 1 - 17 of 17

Citation Count: 13
Bayesian Compressive Sensing For Primary User Detection
(Inst Engineering Technology-IET, 2016) Erküçük, Serhat; Erküçük, Serhat; Cirpan, Hakan Ali
In compressive sensing (CS)-based spectrum sensing literature most studies consider accurate reconstruction of the primary user signal rather than detection of the signal. Furthermore possible absence of the signal is not taken into account while evaluating the spectrum sensing performance. In this study Bayesian CS is studied in detail for primary user detection. In addition to assessing the signal reconstruction performance and comparing it with the conventional basis pursuit approach and the corresponding lower bounds signal detection performance is also considered both analytically and through simulation studies. In the absence of a primary user signal the trade-off between probabilities of detection and false alarm is studied as it is equally important to determine the performance of a CS approach when there is no active primary user. To reduce the computation time and yet achieve a similar detection performance finally the effect of number of iterations is studied for various systems parameters including signal-to-noise-ratio compression ratio mean value of accumulated energy and threshold values. The presented framework in this study is important in the overall implementation of CS-based approaches for primary user detection in practical realisations such as LTE downlink OFDMA as it considers both signal reconstruction and detection.
Citation Count: 6
Bayesian compressive sensing for ultra-wideband channel models
(IEEE, 2012) Erküçük, Serhat; Erküçük, Serhat; Cirpan, Hakan Ali
Considering the sparse structure of ultra-wideband (UWB) channels compressive sensing (CS) is suitable for UWB channel estimation. Among various implementations of CS the inclusion of Bayesian framework has shown potential to improve signal recovery as statistical information related to signal parameters is considered. In this paper we study the channel estimation performance of Bayesian CS (BCS) for various UWB channel models and noise conditions. Specifically we investigate the effects of (i) sparse structure of standardized IEEE 802.15.4a channel models (ii) signal-to-noise ratio (SNR) regions and (iii) number of measurements on the BCS channel estimation performance and compare them to the results of l(1)-norm minimization based estimation which is widely used for sparse channel estimation. The study shows that BCS exhibits superior performance at higher SNR regions only for adequate number of measurements and sparser channel models (e. g. CM1 and CM2). Based on the results of this study BCS method or the l(1)-norm minimization method can be preferred over the other for different system implementation conditions.
Citation Count: 11
Compressive sensing for ultra-wideband channel estimation: on the sparsity assumption of ultra-wideband channels
(Wiley-Blackwell, 2014) Erküçük, Serhat; Erküçük, Serhat; Cirpan, Hakan Ali
Due to the sparse structure of ultra-wideband (UWB) multipath channels there has been a considerable amount of interest in applying the compressive sensing (CS) theory to UWB channel estimation. The main consideration of the related studies is to propose different implementations of the CS theory for the estimation of UWB channels which are assumed to be sparse. In this study we investigate the suitability of standardized UWB channel models to be used with the CS theory. In other words we question the sparsity assumption of realistic UWB multipath channels. For that we particularly investigate the effects of IEEE 802.15.4a UWB channel models and the selection of channel resolution both on channel estimation and system performances from a practical implementation point of view. In addition we compare the channel estimation performance with the Cramer-Rao lower bound for various channel models and number of measurements. The study shows that although UWB channel models for residential environments (e.g. channel models CM1 and CM2) exhibit a sparse structure yielding a reasonable channel estimation performance channel models for industrial environments (e.g. CM8) may not be treated as having a sparse structure due to multipaths arriving densely. Furthermore it is shown that the sparsity increased by channel resolution can improve the channel estimation performance significantly at the expense of increased receiver processing. Copyright (c) 2013 John Wiley & Sons Ltd.
Citation Count: 1
Data-Aided Autoregressive Sparse Channel Tracking for OFDM Systems
(IEEE, 2016) 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 Count: 1
The effect of channel resolution on compressed sensing based Ultra-Wideband channel estimation [Kanal çözünürlü?ünün sıkıştırılmış algılama tabanlı ultra geni?ş bantlı kanal kesti?ri?mi?ne etki?si?]
(2011) Erküçük, Serhat; Erküçük, Serhat; Cirpan, Hakan Ali
Ultra short pulses are transmitted in Ultra-Wideband (UWB) communications. As a result of this pulses at the receiver can be resolved individually due to the sparse structure of the channel. Due to these properties the UWB channel can be estimated by using a compressed sensing based channel estimation method. The performance of channel estimation depends on the sparsity of the channel. In this work the effect of channel resolution which directly affects the sparsity is examined. It is shown that the channel becomes more sparse and the channel estimation performance is improved with increased channel resolution. Also the system performances of practically used selective-Rake receivers with estimated channels are found to be close to the perfect channel estimation case. © 2011 IEEE.
Citation Count: 0
Effect of Inter-Block-Interference-Free Region on Compressed Sensing Based Channel Estimation in TDS-OFDM Systems
(IEEE, 2016) 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 Count: 1
The Effect of Primary User Bandwidth on Bayesian Compressive Sensing Based Spectrum Sensing
(IEEE Computer Society, 2016) Erküçük, Serhat; Erküçük, Serhat; Cirpan, Hakan Ali
The application of compressive sensing (CS) theory has found great interest in wideband spectrum sensing. Although most studies have considered perfect reconstruction of the primary user signal it is actually more important to assess the presence or absence of the signal. Among CS based methods Bayesian CS (BCS) takes into consideration the prior information of signal coefficients to be estimated which improves signal reconstruction performance. On the other hand the sparsity level of the signal to be estimated has a direct impact on signal reconstruction and detection performances. Considering all of the above the effect of sparsity level on BCS based spectrum sensing is studied in this paper. More specifically a BCS based spectrum sensing scheme is considered and its mean-square error (MSE) performance is compared with the Bayesian Cramer-Rao bound for various user bandwidths. BCS MSE is also compared with the deterministic lower MSE (DL-MSE) which is a tight lower bound of the conventional basis pursuit approach. Furthermore complementary receiver operating characteristic (ROC) curves are obtained to examine the trade-off between probabilities of false alarm and detection depending on the user signal bandwidth.
Citation Count: 0
EM-MAP based initialization of PIC receiver for downlink MC-CDMA systems
(IEEE, 2007) Panayırcı, Erdal; Cirpan, Hakan Ali; Panayırcı, Erdal
The quality of multiple access interference (MAI) which can be improved by using channel estimation and data estimation of all active users effects considerably the performance of PIC detector. Therefore data and channel estimation performance is obtained in the initial stage has a significant relationship with the performance of PIC. So obviously it is necessary to make excellent joint data and channel estimation for initialization of PIC detector. In this work to initiate PIC module efficiently a joint MAP channel estimation and data detection technique based on the Expectation Maximization (EM) method has been proposed. Moreover the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The performance of the proposed approach are studied in terms of bit-error rate (BER). Throughout the simulations extensive comparisons with previous works in literature are performed showing that the new scheme can offer superior performance.
Citation Count: 0
Frequency selective fading channel estimation in OFDM systems using KL expansion
(2005) Şenol, Habib; Cirpan, Hakan Ali; 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 Count: 9
Iterative joint data detection and channel estimation for uplink MC-CDMA systems in the presence of frequency selective channels
(2010) Panayırcı, Erdal; Dogan, Hakan; Cirpan, Hakan Ali; Kocian, Alexander; Fleury, Bernard Henri
This paper is concerned with joint multiuser detection and multichannel estimation (JDE) for uplink multicarrier code-division multiple-access (MC-CDMA) systems in the presence of frequency selective channels. The detection and estimation implemented at the receiver are based on a version of the expectation maximization (EM) algorithm and the space-alternating generalized expectation-maximization (SAGE) which are very suitable for multicarrier signal formats. The EM-JDE receiver updates the data bit sequences in parallel while the SAGE-JDE receiver reestimates them successively. The channel parameters are updated in parallel in both schemes. Application of the EM-based algorithm to the problem of iterative data detection and channel estimation leads to a receiver structure that also incorporates a partial interference cancelation. Computer simulations show that the proposed algorithms have excellent BER end estimation performance. Crown Copyright © 2009.
Citation Count: 12
A low-complexity KL expansion-based channel estimator for OFDM systems
(2005) Şenol, Habib; Cirpan, Hakan Ali; Panayırcı, Erdal
This paper first proposes a computationally efficient pilot-aided linear minimum mean square error (MMSE) batch channel estimation algorithm for OFDM systems in unknown wireless fading channels. The proposed approach employs a convenient representation of the discrete multipath fading channel 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. Moreover optimal rank reduction 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 then consider the stochastic Cramér-Rao bound 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. To further reduce the complexity we extend the batch linear MMSE to the sequential linear MMSE estimator. With the fast convergence property and the simple structure the sequential linear MMSE estimator provides an attractive alternative to the implementation of channel estimator.
Citation Count: 5
Monte Carlo Solutions for Blind Phase Noise Estimation
(Springer International Publishing Ag, 2009) Panayırcı, Erdal; Duyck, Dieter; Cirpan, Hakan Ali; Panayırcı, Erdal; Moeneclaey, Marc
This paper investigates the use of Monte Carlo sampling methods for phase noise estimation on additive white Gaussian noise (AWGN) channels. The main contributions of the paper are (i) the development of a Monte Carlo framework for phase noise estimation with special attention to sequential importance sampling and Rao-Blackwellization (ii) the interpretation of existing Monte Carlo solutions within this generic framework and (iii) the derivation of a novel phase noise estimator. Contrary to the ad hoc phase noise estimators that have been proposed in the past the estimators considered in this paper are derived from solid probabilistic and performance-determining arguments. Computer simulations demonstrate that on one hand the Monte Carlo phase noise estimators outperform the existing estimators and on the other hand our newly proposed solution exhibits a lower complexity than the existing Monte Carlo solutions. Copyright (C) 2009 Frederik Simoens et al.
Citation Count: 0
Near field parameter estimation of moving sources with recursive expectation maximization algorithm
(IEEE, 2006) Cekli, Serap; Cekli, Erdinc; Kabaoğlu, Nihat; Cirpan, Hakan Ali
In this paper maximum likelihood (ML) estimator is proposed for the joint estimation of the direction of arrival (DOA) and range parameters of moving sources in the near-field of the antenna array. ML estimation algorithm is presented for deterministic signal model. Recursive form of the expectation maximization (REM) algoritm is suggested for the estimation of the near-field parameters because there is not closed form solutions for the maximum likelihood functions. Moreover simulation results of the suggested algorithm are presented.
Citation Count: 0
Near field parameter estimation of moving sources with recursive expectation maximization algorithm [Yinelemeli beklenti/en büyükleme algoritması ile hareketli kaynakların yakın-alan parametrelerinin kestirimi]
(2006) Çekli, Serap; Çekli, Erdinç; Kabaoğlu, Nihat; Cirpan, Hakan Ali
In this paper maximum likelihood (ML) estimator is proposed for the joint estimation of the direction of arrival (DOA) and range parameters of moving sources in the near-field of the antenna array. ML estimation algorithm is presented for deterministic signal model. Recursive form of the expectation maximization (REM) algoritm is suggested for the estimation of the near-field parameters because there is not closed form solutions for the maximum likelihood functions. Moreover simulation results of the suggested algorithm are presented. © 2006 IEEE.
Citation Count: 4
Pilot-aided bayesian MMSE channel estimation for OFDM systems: Algorithm and performance analysis
(2004) Şenol, Habib; Cirpan, Hakan Ali; Panayırcı, Erdal
This 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 bound 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. © 2004 IEEE.
Citation Count: 0
Rapidly Varying Sparse Channel Tracking with Hybrid Kalman-OMP Algorithm
(Springer, 2019) Şenol, Habib; Erküçük, Serhat; Erküçük, Serhat; Cirpan, Hakan Ali
It is expected from future communication standards that channel estimation algorithms should be able to operate over very fast varying frequency selective channel models. Therefore in this study autoregressive (AR) modeled fast varying channel has been considered and tracked with Kalman filter over one orthogonal frequency division multiplexing (OFDM) symbol. Channel sparsity is exploited which decreases the complexity requirements of the Kalman algorithm. Since Kalman filter is not directly applicable to sparse channels orthogonal matching pursuit (OMP) algorithm is modified for AR modeled sparse signal estimation. Also by using windows sparsity detection errors have been decreased. The simulation results showed that sparse fast varying channel can be tracked with the proposed hybrid Kalman-OMP algorithm and windowing method offers improved MSE results.
Citation Count: 1
Support vector machines based target tracking techniques [Destek vektör makineleri tabanlı hedef takip yöntemleri]
(2006) Özer, Sedat; Cirpan, Hakan Ali; Kabaoğlu, Nihat
This paper addresses the problem of aplying powerful statistical pattern classification algorithms based on kernels to target tracking. Rather than directly adapting a recognizer we develop a localizer directly using the regression form of the Support Vector Machines (SVM). The proposed approach considers using dynamic model together as feature vectors and makes the hyperplane and the support vectors follow the changes in these features. The performance of the tracker is demostrated in a sensor network scenario with a moving target in a polynomial route. © 2006 IEEE.