In this paper we present a new adaptive blind equalization algorithm for multicarrier CDMA systems with single or multiple receive antennas. We analyze the cost function in the well-known subspace method and interpret it in terms of the noise projection matrix. The projection matrix is shown to be a special weighted spectral decomposition of the data autocorrelation matrix, which can be effectively approximated by inverting the data autocorrelation matrix. By adding a user specific correction term to the common cost function, we show that all user channel impulse responses can be estimated in parallel. In this way we develop a block algorithm with low complexity first. We then derive a recursive algorithm using RLS-type matrix updating. Simulations show our recursive algorithm has fast convergence and is near-far resistant. The bit error rate performance is also shown to improve as receiver diversity increases.
KEYWORDS: Algorithm development, Signal to noise ratio, Projection systems, Composites, Acoustics, Interference (communication), Matrices, Data communications, Data modeling, Receivers
This paper presents a new approach for blind reverberation cancellation by adaptively estimating the channels. The key idea of this approach is to exploit the connection between the noise projection matrix and the cost matrix in the well-known subspace approach. A special weighted spectral decomposition is suggested to approximate the noise projection matrix directly from the inverse of the data autocorrelation matrix. We develop an off-line batch algorithm without eigendecomposition first. Combined with RLS-type matrix updating, an on-line adaptive algorithm is derived next to track time-varying channels. Simulations show our methods are robust for speech distorted by FIR reverberation.
The problem of source localization from arrival time delay estimates requires a computationally costly iterative solution of a set of nonlinear equations. Most known methods assume that the propagation speed is known. In this paper, we provide several effective source localization and propagation velocity estimation methods which only use measurements of the relative arrival time delays between sensors. The formulae for source localization and propagation speed estimation are derived based on least squares, total least squares, bounded data uncertainty, and constrained least squares methods. Statistical performance of these methods are compared via computer simulation. In addition, in order to avoid time delay ambiguity problems and obtain smoother time delays, two time delay smoothing methods based on the forward backward algorithm and the Viterbi algorithm are also proposed. Field experiment results based on these techniques are also presented.
KEYWORDS: Deconvolution, Computer simulations, Sensors, Telecommunications, Monte Carlo methods, Quadrature amplitude modulation, Signal processing, Signal to noise ratio, Modulation, Data communications
For single-input multiple-output (SIMO) systems blind deconvolution based on second-order statistics has been shown promising given that the sources and channels meet certain assumptions. In our previous paper we extend the work to multiple-input multiple-output (MIMO) systems by introducing a blind deconvolution algorithm to remove all channel dispersion followed by a blind decorrelation algorithm to separate different sources from their instantaneous mixture. In this paper we first explore more details embedded in our algorithm. Then we present simulation results to show that our algorithm is applicable to MIMO systems excited by a broad class of signals such as speech, music and digitally modulated symbols.
The problems of blind decorrelation and blind deconvolution have attracted considerable interest recently. These two problems traditionally have been studied as two different subjects, and a variety of algorithms have been proposed to solve them. In this paper, we consider these two problems jointly in the application of a multi-sensor network and propose a new algorithm for them. In our model, the system is a MIMO system (multiple-input multiple-output) which consists of linearly independent FIR channels. The unknown inputs are assumed to be uncorrelated and persistently excited. Furthermore, inputs can be colored sources and their distributions can be unknown. The new algorithm is capable of separating multiple input sources passing through some dispersive channels. Our algorithm is a generalization of Moulines' algorithm from single input to multiple inputs. The new algorithm is based on second order statistics which require shorter data length than the higher order statistics algorithms for the same estimation accuracy.
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