Serdar Ozen

A novel channel estimation method: Blending correlation and least-squares based approaches

In this paper we introduce a novel channel estimation method which uses symbol rate sampled signals, based on blending the least squares based channel estimation and the correlation based channel estimation methods. We first overview the shortcomings of the least squares and the correlation based channel estimation algorithm, where a training sequence is utilized in both cases. The performance of the new channel estimation method will be demonstrated, such that the channel estimation will be more robust, and the overall quality of the estimate will be improved by recovering the pulse shape which is naturally embedded in the overall channel impulse response. We will demonstrate how both methods can be combined effectively to minimize the problems associated with the effective channel delay spread being longer than the known training sequence can support.

Structured channel estimation based decision feedback equalizers for sparse multipath channels with applications to digital TV receivers

In this paper, we investigate the performance of channel estimation based equalizers. We introduce two different channel estimation algorithms. Our first channel estimation scheme is a novel structured channel impulse response (CIR) estimation method for sparse multipath channels. The novel CIR estimation method was called blended least squares (BLS) which uses symbol rate sampled signals, based on blending the least squares based channel estimation and the correlation and thresholding based channel estimation methods. The second CIR estimation is called Variable thresholding (VT), and is based on improving the output of the correlation and thresholding based channel estimation method. We then use these two CIR estimates to calculate the decision feedback equalizer (DFE) tap weights. Simulation examples are drawn from the ATSC digital TV 8-VSB system. The delay spread for digital TV systems can 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.

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

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, achieving almost similar performance, with much lower complexity. We first provide a formulation of the received data and correlation processing with the adjacent 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, and the BLUE CIR estimation. Simulation results are provided to demonstrate the performance of the novel algorithms for 8-VSB ATSC digital TV systems.

Conjugate-gradient-based decision feedback equalization with structured channel estimation for digital Television

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.

Time-of-arrival (TOA) estimation based structured sparse channel estimation algorithm, with applications to digital TV receivers

We introduce a new structured channel impulse response (CIR) estimation method for sparse multipath channels where we demonstrate a robust way of restoring the pulse shape into the composite CIR. We call this novel CIR estimation method time-of-arrival based blended least squares (TOA-BLS) which uses symbol rate sampled signals, and it is based on blending correlation processing followed by TOA estimation in the frequency domain by the least squares based channel estimation. TOA estimation in the frequency domain is accomplished by estimating the AR model parameters by solving the forward and forward-backward linear prediction equations in the least squares sense. Simulation examples are drawn from the ATSC digital TV 8-VSB system (ATSC Digital Television Standard, A/53, 1995). The delay spread for digital TV systems can be as long as several hundred times the symbol duration; however, digital TV channels are sparse where there are only a few dominant multipaths.

A novel structured channel estimation method for sparse channels with applications to multi-antenna digital TV receivers

We introduce a novel channel impulse response (CIR) estimation method, for sparse multipath channels, with applications to digital TV receivers with multiple antennas. Our method uses symbol rate samples of the receiver matched filter output, and it is based on blending the least squares based channel estimation and the correlation based channel estimation methods. We first overview the shortcomings of the least squares and the correlation based channel estimation algorithms, where a training sequence is utilized in both cases. The performance of the new channel estimation method is demonstrated, such that the channel estimation becomes more robust, and the overall quality of the estimate improves by recovering the pulse shape which is naturally embedded in the overall channel impulse response. We demonstrate how both methods can be combined effectively to minimize the problems associated with the effective channel delay spread being longer than the known training sequence can support. Examples are drawn from the ATSC digital TV 8-VSB system (see ATSC Digital Television Standard, A/53, 1995) with a multi-antenna receiver. The delay spread for digital TV systems can 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. Finally, we derive the noise variance estimator.

Conjugate gradient based multichannel decision feedback equalization for digital television

We examine the performance of a multichannel decision feedback equalizer (DFE) employing conjugate gradients (CG) based adaptive filtering in the context of digital television based on the 8-VSB standard. The 8-VSB modulation scheme employs real-valued 8-PAM symbols but complex root-raised cosine (spectral) pulse shaping for bandwidth efficiency. Thus, even in the case of a single receive antenna, two virtual channels may be realized via the real and imaginary parts of the received signal. A dual virtual channel minimum mean-square error (MMSE) DFE is derived. Given multiple channels, the high symbol rate of 8-VSB based TV, 10.76 MHz, and the rather large delay spreads one has to contend with in the UHF and VHF bands, the composite DFE weight lies in a space of high dimensionality. We thus implement the MMSE-DFE adaptively using the conjugate gradient (CG) algorithm since it has been shown to effect reduced-rank adaptive filtering, for improved performance with insufficient training, and is also amenable to smart initialization.

Structured channel estimation algorithm based on estimating the time-of-arrivals (TOAs), with applications to digital TV receivers

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.

Approximate best linear unbiased channel estimation for multi-antenna frequency selective channels with applications to digital TV systems

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.

A deterministic ML algorithm for blind joint channel and data estimation with multiple antennas

A maximum likelihood based, iterative, blind data and channel estimation algorithm is presented. The receiver is assumed to have N/sub A/>1 element antenna array. The algorithm that is developed for SIMO communication channels involves least-squares estimation for the channel, and maximum likelihood sequence estimation for the information sequence estimation.