Indexing terms : adaptive algorithm, system identification Abstract : In the presence of input interference, the Wiener solution for impulse response estimation is biased. In this Letter, it is proved that bias removal can be achieved by proper scaling the optimal filter coefficients and a modified least mean squares (LMS) algorithm is then developed for accurate system identification in noise....

This paper proposes a distributed alternating mixed discrete-continuous (DAMDC) algorithm to approach the oracle algorithm based on the diffusion strategy for parameter and spectrum estimation over sensor networks. A least mean squares (LMS) type algorithm that obtains the oracle matrix adaptively is developed and compared with the existing sparsity-aware and conventional algorithms. The propos...

In this paper, we develop a deterministic regularized mixed norm multichannel image restoration algorithm. A functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional using both withinand between-channel deterministic information is proposed. One parameter is defined to control the relative contribution between the LMS and the LMF norms, and...

In scenarios with multiple input single output systems, the stochastic constrained least mean-squares (LMS) algorithm has been proven to be an effective approach. However, when only two input channels are available, it is unclear whether this approach still yields improvements. In this paper, we investigate the stableness and the robustness of the constrained LMS algorithm on “Track 1” of “2 CH...

Smart antenna is the most efficient leading innovation for maximum capacity and improved quality and coverage. A systematic comparison of the performance of different Adaptive Algorithms for beamforming for Smart Antenna System has been extensively studied in this research work. Simulation results revealed that training sequence algorithms like Recursive Least Squares (RLS) and Least Mean Squar...

AbslrabTwo gradient descent adaptive algorithms are compared, the LMS algorithm and the LMSNewton algorithm. LMS is simple and practical, and is used in many applications worldwide. LMWewton is based on Newton's method and the LMS algorithm. LMSiNewton is optimal in the least squares sense. It maximizes the quality of its adaptive solution while minimizing the use of training dah. No other line...

Adaptive filters are employed in many signal processing and communications systems. Commonly, the design and analysis of adaptive algorithms, such as the least mean-squares (LMS) algorithm, is based on the assumptions that the signals are wide-sense stationary (WSS). However, in many cases, including, for example, interference-limited wireless communications and power line communications, the c...

In this paper we present an adaptive BlockBased EigenVector Algorithm (BBEVA) for blind equalization of time-varying multipath fading channels. In addition we assess the performance of the new algorithm for different configurations and compare the results with the least mean squares (LMS) algorithm. The new algorithm is evaluated in terms of intersymbol interference (ISI) suppression, mean squa...

Employing a recently introduced unified adaptive filter theory, we show how the performance of a large number of important adaptive filter algorithms can be predicted within a general framework in nonstationary environment. This approach is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. This general performance analysis ca...

This thesis proposes and studies novel modifications to the least mean squares (LMS) and weighted recursive least squares (WRLS or weighted RLS) adaptive algorithms to estimate the impulse response of a wireless communications channel blindly without the aid of a training or probe sequence. Specifically, we use knowledge of receiver decision quality to weight the LMS and WRLS estimators to incr...