In this paper we have present performance analysis of different equalization techniques such as minimum mean– squared error (MMSE), Least mean square (LMS), Recursive Least Square (RLS) linear equalizers and MMSE-Decision Feedback equalizer (DFE), LMS-DFE, RLS-DFE non-linear equalizers in different channel conditions, in order to achieve optimal equalizer. In our simulation model we have consid...

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...

Least mean square (LMS) adaptive filters have been used in a wide range of one-dimensional signal processing applications. Recently adaptive filtering are presented that are based on the Euclidean Direction Search (EDS) method of optimization. The fast version of this class is called the Fast-EDS or FEDS algorithm. The FEDS based algorithms have a fast convergence rate and O(N) computational co...

Wireless communication systems often suffer problems from multipath and interfering signals. This paper compares two types of adaptive beamforming algorithms for optimising and setting the weights of smart antenna systems, namely the Least Mean Square algorithm (LMS) and Sample Matrix Inversion (SIM). The direction of the main beam of the antenna system is adjusted to enhance the desired signal...

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract – In this paper we describe adaptive noise cancellation and demonstrate it using real time speech signals. The adaptive filter design discussed here is based on two algorithms RLS (Recursive Least Square) and LMS (Least Mean Square) and a compariso...

In this paper, the Acoustic Echo Cancellation (AEC) are investigated by using Finite Impulse Responses Adaptive Filter with the analysis of Mean Square Error (MSE) and its convergence property. It is the result of a project in the course Fundamental of Signal Processing at Chongqing University of Posts and Telecommunications. It focuses on Normalized Least Mean Square (NLMS) algorithm of adapti...

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...

Global position systems (GPS) receiver based on least mean square (LMS) could resist the interference by null jamming direction. Considering that existing band-limited Gaussian noise signals were easily suppressed LMS-based GPS receivers, a time domain random flicker algorithm was proposed to improve its performance. By disturbing convergence of LMS, it achieve purpose suppressing GPS. Simulati...