A novel approach for the least-mean-square (LMS) estimation algorithm is proposed. Rather than using a fixed convergence parameter μ, this approach utilizes a time-varying LMS parameter μn. This technique leads to faster convergence and provides reduced mean-squared error compared to the conventional fixed parameter LMS algorithm. The algorithm has been tested for noise reduction and estimation...

In this paper, we present the convergence analysis of proportionate-type least mean square (Pt-LMS) algorithm that identifies the sparse system effectively and more suitable for real time VLSI applications. Both first and second order convergence analysis of Pt-LMS algorithm is studied. Optimum convergence behavior of Pt-LMS algorithm is studied from the second order convergence analysis provid...

This paper presents the convergence analysis result of the discrete cosine transform-least-mean-square (DCT-LMS) adaptive "ltering algorithm which is based on a well-known interpretation of the variable stepsize algorithm. The time-varying stepsize of the DCT-LMS algorithm is implemented by the modi"ed power estimator to redistribute the spread power after the DCT. The performance analysis is c...

The newly proposed l1 norm constraint zero-point attraction Least Mean Square algorithm (ZA-LMS) demonstrates excellent performance on exact sparse system identification. However, ZA-LMS has less advantage against standard LMS when the system is near sparse. Thus, in this paper, firstly the near sparse system modeling by Generalized Gaussian Distribution is recommended, where the sparsity is de...

Adaptive equalizer is important in transmission of wireless communication. The equalizer using least mean square (LMS) algorithm is adopted. Simulation results show that step size influences the algorithm convergence and stability, which will significantly affect the performance of adaptive equalizer. The requirement of step for convergence speed, time-varying tracking accuracy and convergence ...

Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational complexity and robust recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., L1-norm LMS or zero-attracting LMS (sparse LMS or ZA-LMS), reweighted zero attracting LMS (RZA-LMS) and Lp-norm LMS (LP-LMS), have been proposed b...

In this paper, a novel noncoherent adaptive algorithm for channel estimation is introduced. The proposed noncoherent least–mean–square (NC–LMS) algorithm can be combined easily with noncoherent sequence estimation (NSE) for M–ary differential phase–shift keying (MDPSK) signals transmitted over intersymbol interference (ISI) channels. For zero frequency offset the convergence speed and the stead...

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...

This paper presents a simple and easy implementable Least Mean Square (LMS) type approach for frequency estimation of three phase power system in an unbalanced condition. The proposed LMS type algorithm is based on a second order recursion for the complex voltage derived from Clarke's transformation which is proved in the paper. The proposed algorithm is real adaptive filter with real parameter...

In this paper, we present the multi-split version of the widely linear LMS algorithm. As in conventional linear filtering, the multi-split transform increases the diagonalization factor of the composed autocorrelation and pseudoautocorrelation matrix of the improper input signal, and a power normalized and time-varying step-size LMS algorithm is used for updating the filter parameters. Simulati...