Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Conference Object Ofdm Sistemler için Pilot Tabanlı Yinelemeli Kanal Kestirimi(IEEE, 2008) Baştürk, İlhan; Özbek, BernaIn this study, pilot-based iterative channel estimation, which use virtual pilots, is proposed for OFDM systems In order to find final channel estimates, instead of averaging the group estimates, which are found by virtual pilots, it is proposed to combine them by taking into account their reliabilities that are calculated by using probability density function. Then the proposed method is compared to Expectation-Maximization (EM) algorithm with respect to performance and computational complexity. The performance results are given in terms of Bit Error Rate (BER) and Mean Squared Error (MSE). ©2008 IEEE.Conference Object Approximate Best Linear Unbiased Channel Estimation for Multi-Antenna Frequency Selective Channels With Applications To Digital Tv Systems(SPIE, 2004) Özen, Serdar; Pladdy, Christopher; Nerayanuru, Sreenivasa M.; Fimoff, Mark J.; Zoltowski, Michael D.We provide an iterative and a non-iterative channel impulse response (CIR) estimation algorithm for communication receivers with multiple-antenna. Our algorithm is best suited for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols, and the receivers for this particular system are expected work in a severe frequency selective multipath environment with long delay spreads relative to the length of the training sequence. The iterative procedure calculates the (semi-blind) Best Linear Unbiased Estimate (BLUE) of the CIR. The non-iterative version is an approximation to the BLUE CIR estimate, denoted by a-BLUE, achieving almost similar performance, with much lower complexity. Indeed we show that, with reasonable assumptions, a-BLUE channel estimate can be obtained by using a stored copy of a pre-computed matrix in the receiver which enables the use of the initial CIR estimate by the subsequent equalizer tap weight calculator. Simulation results are provided to demonstrate the performance of the novel algorithms for 8-VSB ATSC Digital TV system. We also provide a simulation study of the robustness of the a-BLUE algorithm to timing and carrier phase offsets.Conference Object Citation - WoS: 1Citation - Scopus: 3Iterative Em-Based Channel Estimation for Stbc-Ofdm(Institute of Electrical and Electronics Engineers Inc., 2009) Baştürk, İlhan; Özbek, BernaIn this paper, an iterative EM based channel estimation algorithm is studied for STBC-OFDM systems. Compared to the time domain EM based channel estimation algorithm which needs matrix inversion, a frequency domain EM based channel estimation algorithm is proposed by estimating the channel coefficients for each subcarrier. The proposed channel estimation algorithm decreased the complexity without sacrificing the performance. The time domain and proposed frequency domain EM based channel estimation algorithms are compared in terms of bit error rate (BER), mean square error (MSE) and the number of iterations used in the EM algorithm.Article Taylor Series Approximation of Semi-Blind Blue Channel Estimates With Applications To Dtv(Taylor and Francis Ltd., 2008) Pladdy, Christopher; Özen, Serdar; Nerayanuru, Sreenivasa M.; Ding, Peilu; Fimoff, Mark J.; Zoltowski, MichaelWe present a low-complexity method for approximating the semi-blind best linear unbiased estimate (BLUE) of a channel impulse response (CIR) vector for a communication system, which utilizes a periodically transmitted training sequence. The BLUE, for h, for the general linear model, y = Ah + w + n, where w is correlated noise (dependent on the CIR, h) and the vector n is an Additive White Gaussian Noise (AWGN) process, which is uncorrelated with w is given by h = (ATC(h)-1A)-1ATC(h)-1y. In the present work, we propose a Taylor series approximation for the function F(h) = (ATC(h)-1A)-1ATC(h)-1y. We describe the full Taylor formula for this function and describe algorithms using, first-, second-, and third-order approximations, respectively. The algorithms give better performance than correlation channel estimates and previous approximations used, at only a slight increase in complexity. Our algorithm is derived and works within the framework imposed by the ATSC 8-VSB DTV transmission system, but will generalize to any communication system utilizing a training sequence embedded within data.Conference Object Citation - WoS: 2Citation - Scopus: 2Taylor Series Approximation of Semi-Blind Best Linear Unbiased Channel Estimates for the General Linear Model(Institute of Electrical and Electronics Engineers Inc., 2004) Pladdy, Christopher; Nerayanuru, Sreenivasa M.; Fimoff, Mark; Özen, Serdar; Zoltowski, MichaelWe present a low complexity approximate method for semi-blind best linear unbiased estimation (BLUE) of a channel impulse response vector (CIR) for a communication system, which utilizes a periodically transmitted training sequence, within a continuous stream of information symbols. The algorithm achieves slightly degraded results at a much lower complexity than directly computing the BLUE CIR estimate. In addition, the inverse matrix required to invert the weighted normal equations to solve the general least squares problem may be pre-computed and stored at the receiver. The BLUE estimate is obtained by solving the general linear model, y = Ah + w + n, for h, where w is correlated noise and the vector n is an AWGN process, which is uncorrelated with w. The Gauss - Markoff theorem gives the solution h = (A TC(h) -1A) -1A TC(h) -1y. In the present work we propose a Taylor series approximation for the function F(h) = (A TC(h) -1A) -1A TC(h) -1y where, F:R L → R L for each fixed vector of received symbols, y, and each fixed convolution matrix of known transmitted training symbols, A. We describe the full Taylor formula for this function, F(h) = F(h id) + ∑|α|≥|(h - h id) α(∂/∂h) αF(h id) and describe algorithms using, respectively, first, second and third order approximations. The algorithms give better performance than correlation channel estimates and previous approximations used, [15], at only a slight increase in complexity. The linearization procedure used is similar to that used in the linearization to obtain the extended Kaiman filter, and the higher order approximations are similar to those used in obtaining higher order Kaiman filter approximations,