In this paper an adaptation technique using both genetic algorithm (GA) and least mean square algorithm (LMS) is proposed. The key control parameters tap-length and tap-weight are updated using the GA and the LMS algorithm respectively according to the value of mean square error (MSE). The proposed algorithm results optimal solutions very fast by reducing initialization and derivative problems....

The paper presents a comparative study of NLMS (Normalized Least Mean Square), NVSS (New Variable Step Size) LMS (Least Mean Square), RVSS (Robust Variable Step Size) LMS, TVLMS (Time Varying Least Mean Square) and IVSS (Improved Variable Step Size) LMS adaptive filter algorithms. Four performances criterion are utilized in this study: Minimum Mean Square Error (MSE), Convergence Speed, Algorit...

In this work, a new class of stochastic gradient algorithm is developed based on q-calculus. Unlike the existing q-LMS algorithm, the proposed approach fully utilizes the concept of q-calculus by incorporating time-varying q parameter. The proposed enhanced q-LMS (Eq-LMS) algorithm utilizes a novel, parameterless concept of error-correlation energy and normalization of signal to ensure high con...

Channel estimation problem is one of key technical issues in time-variant multiple-input multiple-output (MIMO) communication systems. To estimate the MIMO channel, least mean square (LMS) algorithm was applied to adaptive channel estimation (ACE). Since the MIMO channel is often described by sparse channel model, such sparsity could be exploited and then estimation performance could be improve...

The paper revisits adaptive beamforming assisted receiver for multiple antenna aided multiuser systems that employ binary phase shift keying (BPSK) modulation. The standard minimum mean square error (MMSE) design is based on the criterion of minimising the mean square error (MSE) between the beamformer’s desired output and complex-valued beamformer’s output. Since the desired output for BPSK sy...

This paper prefers Acoustic Noise cancellation (ANC) system using Wavelet based adaptive filtering algorithms. The Acoustic Noise canceller is implemented using adaptive algorithms like LMS (Least Mean Square), NLMS (Normalized Least Mean Square),RLS (Recursive Least Square), and FRLS (Fast Recursive Least Square). The inclusion of wavelet based transformation in ANC reduces the number of sampl...

Least mean square-partial parallel interference cancelation (LMS-PPIC) is a partial interference cancelation using adaptive multistage structure in which the normalized least mean square (NLMS) adaptive algorithm is engaged to obtain the cancelation weights. The performance of the NLMS algorithm is mostly dependent to its step-size. A fixed and non-optimized step-size causes the propagation of ...

Though the Leaky Least-Mean-Square (LLMS) algorithm mitigates the drifting problem of the LMS algorithm, its performance is similar to that of the LMS algorithm in terms of convergence rate. In this paper, we propose a new LLMS algorithm that has a better performance than the LLMS algorithm in terms of the convergence rate and at the same time solves the drifting problem in the LMS algorithm. T...

This contribution proposes a minimum bit error rate (MBER) decision feedback equaliser (DFE) designed for single-input multiple-output (SIMO) systems employing a quadrature phase shift keying (QPSK) modulation scheme. It is shown that this MBER design is superior over the standard minimum mean square error DFE in the SIMO scenario considered, in terms of the achievable system bit error rate. A ...

ECG signals have been proven a very versatile tool for detection of cardiovascular diseases. But during recording of these signals, the ECG data gets contaminated by various noise signals caused by power line interference, base line wander, electrode movement, muscle movement (EMG) etc. These noise signals are known as artifacts. These artifacts mislead the diagnosis of heart which is not desir...