A statistical model for the range error provided by TOA estimation using UWB signals is given, based on UWB channel measurements between 3.1 and 10.6 GHz. The range error has been modeled as a Gaussian random variable for LOS and as a combination of a Gaussian and an exponential random variable for NLOS. The distance and bandwidth dependency of both the mean and the standard deviation of the ra...

In this paper, a receiver structure which combines multiuser detection (temporal filtering) and receiver beamforming (spatial filtering) in a multipath environment is considered. Following [1], [2], we model the receiver as a linear matrix filter and use the minimum mean-squared error (MMSE) as the performance criterion. Motivated by the complexity of the optimum receiver, we propose rank const...

This paper investigates the properties of the performance surface for the problem of nonlinear mean-square estimation of a random sequence. The problem studied has direct application to the study of active noise control (ANC) systems when the transducers are driven into a nonlinear behavior. A deterministic expression is derived for the mean-square error (MSE) surface as a function of the syste...

Least mean square (LMS) type adaptive algorithms have attracted much attention due to their low computational complexity. In the scenarios of sparse channel estimation, zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS) and reweighted -norm LMS (RL1-LMS) have been proposed to exploit channel sparsity. However, these proposed algorithms may hard to make tradeoff between convergence speed ...

For many applications such as acoustic echo compensation, adaptive noise reduction or acoustic feedback control it is of great interest to simulate reproducibly a real, time variant room. One approach to describe the transient behavior of a room is the generation of a physical room model, e.g. [l]. To identify the variation of a room impulse response with time an alternative concept is presente...

An unbiased impulse response estimation approach is applied to time delay estimation between noisy signals received at two spatially separated sensors. The estimated delay is modeled by an adaptive "nite impulse response "lter whose coe,cients are updated according to a modi"ed least mean square (LMS) scheme. Simulation results show that the proposed algorithm outperforms the conventional LMS t...

The channel estimation is one of important techniques to ensure reliable broadband signal transmission. Broadband channels are often modeled as a sparse channel. Comparing with traditional dense-assumption based linear channel estimation methods, e.g., least mean square/fourth (LMS/F) algorithm, exploiting sparse structure information can get extra performance gain. By introducing -norm penalty...

The problem of regulating the transmission rate of an available bit rate (ABR) traÆc source in an ATM network is examined. Of particular interest is linear quadratic (LQ) rate regulation based on estimates of the round-trip propagation delay. The round trip delay is estimated using a nonlinear least mean square (NLMS) algorithm. Simulation results are used to demonstrate the method.

In this paper, a simplified model is used to qualitatively and quantitatively analyze two normalized data-reusing LMS algorithms when the input sequence is colored. The proposed approach relates the eigenvalue distribution of the input-signal autocorrelation matrix with probabilities of occurrence of subsequent parallel or orthogonal input-signal vectors. Closed-form formulas that describe stea...

It is well-known that constant-modulus-based algorithms present a large mean-square error for high-order quadrature amplitude modulation (QAM) signals, which may damage the switching to decision-directed-based algorithms. In this paper, we introduce a regional multimodulus algorithm for blind equalization of QAM signals that performs similarly to the supervised normalized least-mean-squares (NL...