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.
In this paper we introduce a new structured channel impulse
response (CIR) estimation method for sparse multipath channels. We
call this novel CIR estimation method Time-Of-Arrival based Blended Least Squares (TOA-BLS) which uses symbol rate sampled signals, based on blending the least squares based channel estimation and the correlation and cleaning followed by TOA estimation. TOA estimation is accomplished in the frequency domain and is based on AR model parameter estimation via unconstrained least squares. Simulation examples are drawn from the ATSC digital TV 8-VSB system. The delay spread for digital TV systemscan be as long as several hundred times the symbol duration; however digital TV channels are, in general, sparse where there are only a few dominant multipaths.
In this paper, we show how the convergence time of equalizers for 8-VSB based on the conjugate gradient (CG) algorithm can be considerably improved through initialization based on a channel estimate. We derive real and complex minimum mean-square error (MMSE) equalizers and implement them adaptively using the conjugate gradient, recursive least squares (RLS), and least mean squares (LMS) algorithms. We show that both CG and RLS have similar convergence times --- both are much faster than LMS. Since the CG algorithm is easily initialized, we compare several methods of initialization to determine how each affects convergence and then apply the best methods to initialize equalizers using channel estimates. We find that initializing the correlation matrices and filling the feedback taps with training symbols greatly speeds convergence of the CG adaptive equalizer, potentially approaching the rate of convergence when running the algorithm on the matrix equations using the actual channel.
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