Semiblind BLUE Channel Estimation With Applications to Digital Television

Abstract

Abstract-A semiblind iterative algorithm to construct the best linear unbiased estimate (BLUE) of the channel impulse response (CIR) vector h for communication systems that utilize a periodically transmitted training sequence within a continuous stream of information symbols is devised. The BLUE CIR estimate for the general linear model y = Ah + w, where w is the correlated noise, is given by the Gauss-Markoff theorem. The covariance matrix of the correlated noise, which is denoted by C(h), is a function of the channel that is to be identified. Consequently, an iteration is used to give successive approximations h (k) , k = 0, 1, 2, . . . to h BLUE , where h (0) is an initial approximation given by the correlation processing, which exists at the receiver for the purpose of frame synchronization. A function F (h) for which h BLUE is a fixed point is defined. Conditions under which h BLUE is the unique fixed point and for which the iteration proposed in the algorithm converges to the unique fixed point h BLUE are given. The proofs of these results follow broadly along the lines of Banach fixed-point theorems.

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

“…Some of the material contained in sections 3 and 4 is also present in [16]. We include it here for completeness.…”

“…In [27], an approximate version of the iterative algorithm of [15] and [16] is described. In the present work, we propose a more general framework within which the approximation given in [27] would be the zero-order Taylor series approximation of the function F(h), i.e., a constant approximation, F(h id ), to the function F(h).…”

“…Our algorithm allows for such accurate channel estimation, even in a mobile setting. In [15] and [16], we devised a semi-blind iterative algorithm to construct the best linear unbiased estimate (BLUE) of the channel, which is given by the Gauss-Markoff Theorem ( [17][18][19]) as…”