Serdar Özen

Best linear unbiased channel estimation for frequency selective multipath channels with long delay spreads

We provide an iterative channel impulse response (CIR) estimation algorithm for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols. This iterative procedure calculates the (semi-blind) best linear unbiased estimate (BLUE) of the CIR. We first provide a formulation of the received data and correlation processing with the adjacent information symbol correlation taken into account, and we then present the connections of the correlation based CIR estimation scheme to the ordinary least squares CIR estimation. Simulation results are provided to demonstrate the performance of the novel algorithm.

A fading filter design for multipath Rayleigh fading simulation and comparisons to other simulators

A low-complexity high performance Rayleigh fading simulator, an ARMA(3,3) model, is proposed. This proposed method is a variant of the method of filtering of the white Gaussian noise where the filter design is accomplished in the analog domain and transferred into digital domain. The proposed model is compared with improved Jakespsila model, autoregressive filtering and IDFT techniques, in performance and computational complexity. Proposed method outperforms AR(20) filter and modified Jakespsila generators in performance. Although IDFT method achieves the best performance, it brings a significant cost in storage and is undesirable. The proposed method achieves high performance with the lowest complexity.

Fast communication: Constraint removal for sparse signal recovery

This paper presents a new iterative algorithm called constraint removal (CR) for the recovery of a sparse signal x from an incomplete number of linear measurements y such that y^m^x^1=A^m^x^nx^n^x^1 and m

Taylor series approximation for low complexity semi-blind best linear unbiased channel estimates for the general linear model with applications to DTV

We 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 inverting the weighted normal equations to solve the general least squares problem may be precomputed 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 solution is given by the Gauss-Markoff theorem as h = (A/sup T/C(h)/sup -1/A)/sup -1/ A/sup T/C(h)/sup -1/y. In the present work we propose a Taylor series approximation for the function F(h) = (A/sup T/C(h)/sup -1/A)/sup -1/ A/sup T/C(h)/sup -1/y where, F : R/sup L/ /spl rarr/ R/sup 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/sub id/) + /spl Sigma//sub |/spl alpha/|/spl ges/1/ (h - h/sub id/)/sup /spl alpha// (/spl part///spl part/h)/sup /spl alpha// F (h/sub 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, (S. Ozen, et al., 2003), at only a slight increase in complexity. The linearization procedure used is similar to that used in the linearization to obtain the extended Kalman filter, and the higher order approximations are similar to those used in obtaining higher order Kalman filter approximations, (A. Gelb, et al., 1974).

Taylor series approximation of semi-blind BLUE channel estimates with applications to DTV

We 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)−1 A)−1 ATC(h)−1 y. In the present work, we propose a Taylor series approximation for the function F(h) = (ATC(h)−1 A)−1 ATC(h)−1 y. 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.

Approximate best linear unbiased channel estimation for frequency selective channels with long delay spreads: robustness to timing and carrier offsets

We provide an iterative and a non-iterative channel impulse response (CIR) estimation algorithm for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols. 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 the 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.