Performance Analysis of l_0 Norm Constraint Least Mean Square Algorithm

As one of the recently proposed algorithms for sparse system identification, $l_0$ norm constraint Least Mean Square ($l_0$-LMS) algorithm modifies the cost function of the traditional method with a penalty of tap-weight sparsity. The performance of $l_0$-LMS is quite attractive compared with its various precursors. However, there has been no detailed study of its performance. This paper presents all-around and throughout theoretical performance analysis of $l_0$-LMS for white Gaussian input data based on some reasonable assumptions. Expressions for steady-state mean square deviation (MSD) are derived and discussed with respect to algorithm parameters and system sparsity. The parameter selection rule is established for achieving the best performance. Approximated with Taylor series, the instantaneous behavior is also derived. In addition, the relationship between $l_0$-LMS and some previous arts and the sufficient conditions for $l_0$-LMS to accelerate convergence are set up. Finally, all of the theoretical results are compared with simulations and are shown to agree well in a large range of parameter setting.Comment: 31 pages, 8 figure

ECG signal denoising using a novel approach of adaptive filters for real-time processing

Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper

Two Channel Estimation Methods for MIMO-OFDM System

Adaptive Filter is a part of the modern communication system. The applications of the adaptive filters are channel equalization, noise cancellation, system identification and adaptive beam forming. So the proper implementation of adaptive filter is a great deal. The intersymbol interference (ISI) caused by the multipath in band limited frequency selective time dispersion channel distort the transmitted signal.  In this paper, we have concentrated on modifying the algorithm for the adaptive filter. The proposed VSS-LLMS and Modified Variable Step Size Leaky LMS (MVSS-LLMS) which improves the channel estimation in the noisy environment. Also we compared the results of our proposed algorithms with the LMS, RLS and VLLMS and observed that it improves in computational complexity and Bit Error Rate (BER) performance. Keywords: Adaptive Channel Estimation, Adaptive filter, LMS, RLS, VLLMS and MVSS-LLM

Randomly Spaced Smart Antennas

The goal of this thesis is to develop adaptive array\u27 features out of randomly spaced antenna elements. Most optimization techniques that have been presented so far for non-equidistant antenna arrays have been restricted to the analysis of symmetric or linear geometries. Several direction of arrival (DOA) and adaptive beamforming al- gorithms are implemented and analyzed for both linear and planar randomly spaced antenna configurations; such as Capon and MUSIC for direction of arrival, and LMS, normalized LMS, leaky LMS, generalized normalized LMS, RLS and variable forget- ting factor RLS algorithms for the adaptive beamforming. The advantages and disadvantages of smart antennas based on random array con- figuration are presented and discussed. Several algorithms have been investigated to study the most suitable ones for optimizing the distribution of the excitation coe- cients. The work discussed herein can be extended to space applications using a cluster of small satellites, UAVs, or any array of sensors that are not aligned together in a standard linear or planar geometrical configuration. Furthermore, the proposed approach can also be extended to standard, two-dimensional linear arrays when one or more elements fail.\u2