In this study, we present a measurement-based model for path loss prediction in three GSM service areas at 900 MHz . Modified Hata model for rural, suburban, and urban environments were derived in this study on the basis of experimental path loss measurements with the use of least square method. The models developed predicted with reasonable accuracy the path loss of radio networks investigated...

A CMOS switched-current adaptive filter architecture is presented. It is basically a finite impulse response (FIR) transversal filter adapted by using the Least-Mean-Square (LMS) adaptation algorithm. The design is based on delay and multiply-accumulator blocks. The implemented system can be switched to work either as an adaptive filter or as an FIR programmable filter. The adaptation signal ca...

This article present a new method of accuracy verification of calibrated one-port vector network analyzers based on the least mean square algorithm. The method is of particular interest for significantly limited frequency ranges as well as for cost-effective S-parameter measurement systems where the use of conventional ripple test may be impractical and/or relatively expensive. Experimental stu...

The sign error observer algorithm introduced in the paper is based on the relationship between the least mean square (LMS) [1] and resonator based observer algorithms [2][3], but utilizes the sign error LMS algorithm [4] for estimating the state variables of the observed system. Since the algorithm uses the signum of the error of the estimation, significant reduction in the amount of data requi...

To exploit the sparsity of the considered system, the diffusion proportionate-type least mean square (PtLMS) algorithms assign different gains to each tap in the convergence stage while the diffusion sparsity-constrained LMS (ScLMS) algorithms pull the components towards zeros in the steady-state stage. In this paper, by minimizing a differentiable cost function that utilizes the Riemannian dis...

Electrocardiogram (ECG) signal is affected by many noise interferences. Out of all the noise effects the power line interference is the predominant one. In this paper the implementation of the adaptive algorithm techniques for reduction in this power line interference is shown and a comparison of these techniques is performed. The adaptive filters used have shown a good improvement in the SNR (...

Adaptive estimation of the linear coefficient vector in truncated expansions is considered for the purpose of modeling noisy, recurrent signals. The block LMS (BLMS) algorithm, being the solution of the steepest descent strategy for minimizing the mean square error in a complete signal occurrence, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. It is demon...

An augmented complex least mean square (ACLMS) algorithm for complex domain adaptive filtering which utilises the full second order statistical information is derived for adaptive prediction problems. This is achieved based on some recent advances in complex statistics and by using widely linear modelling in C. This way, both circular and non–circular complex signals can be processed optimally,...

A new Lp-norm constraint least mean square (Lp-LMS) algorithm with new strategy of varying p is presented, which is applied to system identification in this letter. The parameter p is iteratively adjusted by the gradient method applied to the root relative deviation of the estimated weight vector. Numerical simulations show that this new algorithm achieves lower steady-state error as well as eq...

This paper presents a unifying view of various error nonlinearities that are used in least mean square (LMS) adaptation such as the least mean fourth (LMF) algorithm and its family and the least-mean mixed-norm algorithm. Speci cally, it is shown that the LMS algorithm and its errormodi ed variants are approximations of two recently developed optimum nonlinearities which are expressed in terms ...