Pladdy, ChristopherÖzen, SerdarÖzen, SerdarNerayanuru, Sreenivasa M.Ding, PeiluFimoff, Mark J.Zoltowski, Michael03.05. Department of Electrical and Electronics Engineering03. Faculty of Engineering01. Izmir Institute of Technology2016-11-112016-11-112008-01Pladdy, C., Özen, S., Nerayanuru, S. M., Ding, P., Fimoff, M. J., and Zoltowski, M. (2008). Taylor series approximation of semi-blind BLUE channel estimates with applications to DTV. Inverse Problems in Science and Engineering, 16(3), 303-324. doi:10.1080/174159707017433501741-5977http://doi.org/10.1080/17415970701743350http://hdl.handle.net/11147/2428We 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.eninfo:eu-repo/semantics/openAccessChannel estimationBest linear unbiased estimationGauss Markoff TheoremTaylor series approximationLinearizationArticle2-s2.0-4244913249310.1080/17415970701743350