Performance Analysis of Adaptive Decision Feedback Turbo Equalization (ADFTE) Using Recursive Least Square (RLS) Algorithm over Least Mean Square (LMS) Algorithm

2007 ◽  
Author(s):  
Suneeta V Budihal ◽  
R.M. Banakar
Keyword(s):  
Performance Analysis ◽  
Turbo Equalization ◽  
Least Square ◽  
Decision Feedback ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Rls Algorithm

scholarly journals Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mahmoud Aldababseh ◽  
Ali Jamoos
Keyword(s):  
Fading Channels ◽  
Kalman Filters ◽  
Fading Channel ◽  
Minimum Mean Square Error ◽  
Least Square ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Channel Statistics

We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method.

scholarly journals FIELD PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF A VARIABLE LEAKY LEAST MEAN SQUARE ADAPTIVE ALGORITHM

2017 ◽  
Vol 10 (13) ◽  
pp. 69
Author(s):  
Purvika Kalkar ◽  
John Sahaya Rani Alex
Keyword(s):  
Field Programmable Gate Array ◽  
Noise Cancellation ◽  
Least Square ◽  
Lms Algorithm ◽  
Statistical Characteristics ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Field Programmable ◽  
Gate Array

Adaptive noise cancellation is an extensively researched area of signal processing. Many algorithms had been studied such as least mean square algorithm (LMS), recursive least square algorithm, and normalized LMS algorithm. The statistical characteristics of noise are fast in nature and the algorithms for noise cancellation should converge fast. Since LMS algorithm has slow convergence; in this paper, a variable leaky LMS (VLLMS) algorithm is explored. VLLMS is implemented using the concept of hardware-software cosimulation using Xilinx System Generator. The design is implemented on Virtex-6 ML605 field programmable gate array board. The implemented design is tested for sinusoidal signal added with an additivewhite Gaussian noise. The design summary and the utilization summary are presented. 

scholarly journals An Inclusive Survey on Various Adaptive Beam Forming Algorithm for 5G Communications Systems

2020 ◽  
Author(s):  
Kaviya K R ◽  
Deepa S
Keyword(s):  
Smart Antenna ◽  
Minimum Variance ◽  
Antenna System ◽  
Least Square ◽  
Decision Feedback ◽  
Signal Interference ◽  
Beam Forming ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square

There are several existing wireless system in 5G technology, originating interference in same frequency band and degenerate the concert of received signal. Antenna System comprise of different Beam forming methods in which direction of required signal is generated by the beam and nulls and the voids are set in the direction of unwanted signal (Interference). The survey of different blind and non-blind beam forming algorithms are discussed using smart antenna and phased array. It involves Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS), Sample Matrix Inversion(SMI), Linear Constrained Minimum Variance (LCMV), Constant Modulus (CMA), Decision feedback equalization based LMS (DFE-LMS) are considered. These algorithms are outlined to be claimed in 5G network to provide good quality, capacity and dealing with coincidence of signals and interference.

Investigation of Phase Noise on the Performance of LMS-RLS Adaptive Equalizer

2013 ◽  
Vol 6 (1) ◽  
pp. 27-35
Author(s):  
Radhi Shabib Kaned
Keyword(s):  
Phase Noise ◽  
Bit Error Rate ◽  
Communication Channels ◽  
Adaptive Algorithms ◽  
Least Square ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Adaptive Equalizer ◽  
Rls Algorithm

This paper investigates the effect of phase noise on equalization of  communication channels using least mean square (LMS) and recursive least square (RLS) adaptive algorithms. The aim of the investigation is to mitigate inter-symbol interference (ISI) caused by the channel and to impose the bit error rate (BER) in the received signals. The equalizerusestwobasicadaptivealgorithms: LMS algorithmand RLS algorithm. Without LMS-RLS equalizer,theBER ismorethan when the system modelincludesLMS-RLS equalizer as indicated in table (1) and table (2). Equalizer algorithm is analyzed using MATLAB v.9 Communication Block Set.

Noise Reduction in RTL-SDR using Least Mean Square and Recursive Least Square

2020 ◽  
Vol 4 (2) ◽  
pp. 286-295
Author(s):  
Aviv Yuniar Rahman ◽  
Mamba’us Sa’adah ◽  
Istiadi
Keyword(s):  
Noise Reduction ◽  
Software Defined Radio ◽  
Convergence Speed ◽  
Least Square ◽  
Radio Station ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Radio Communication

Noise reduction is an important process in a communication system, one of which is radio communication. In the process of broadcasting radio Frequency Modulation (FM) often encountered noise so that listeners find it difficult to understand the information provided. In the past, noise reduction used traditional filters that were only able to filter certain frequencies. However, for future technologies an adaptive filter is needed that can dynamically reduce noise effectively. Register Level-Software Defined Radio (RTL-SDR) can capture signals with a very wide frequency range but has a less clear sound quality. So it needs to be done noise reduction. In this study, two methods are used, namely Least Mean Square (LMS) and Recursive Least Square (RLS). The data used five radio stations in Malang. The results showed that the LMS algorithm is stable but has a slow convergence speed, whereas the RLS algorithm has poor stability but has a high convergence speed. From the test, it can be concluded that the performance of RLS is better than LMS for noise reduction in RTL-SDR. The best performance is the reduction of White Noise using RLS on the Oryza radio station with an Normalized Weight Differences (NWD) value of -13.93 dB.

Adaptive IIR Filtering Techniques for Dynamic Modeling of a Twin Rotor System

Volume 1 ◽  
2004 ◽  
Author(s):  
M. O. Tokhi ◽  
M. S. Alam ◽  
F. M. Aldebrez
Keyword(s):  
Input Signal ◽  
Learning Algorithms ◽  
Dynamic Modelling ◽  
Least Square ◽  
Infinite Impulse Response ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Twin Rotor ◽  
Filtering Techniques

This paper investigates the development of a parametric model to characterise pitch movement in a twin rotor multi-input multi-output system (TRMS) using adaptive infinite impulse response (IIR) models. The TRMS is a laboratory platform designed for control experiments. In certain aspects, its behaviour resembles that of a helicopter. It typifies a high-order nonlinear system with significant cross coupling between its two channels. It also simulates similar problems and challenges encountered in real systems. These include complex dynamics that lead to both parametric and dynamic uncertainty, unmeasurable states and sensor and actuator noise. In this work, adaptive IIR filtering techniques using least mean square (LMS) and recursive least square (RLS) algorithms are investigated for dynamic modelling of the system. The system is initially excited with random gaussian sequence input signal of sufficient bandwidth (0–10Hz) to ensure that all resonance modes of interest are captured. The magnitude of the input signal is selected so that it does not drive the system out of its linear operating range. Good excitation is achieved from 0–2.5 Hz, which includes all the important rigid body and flexible modes. Then, adaptive IIR filters based on equation error formulation are used for modelling the system. Three standard algorithms; namely, LMS, normalized LMS and RLS are utilized as learning algorithms, to update the parameters of the filter during the modelling process. A comparative assessment of the three learning algorithms, in characterising the system, is conducted. The performance of each model is assessed in terms of output tracking, minimization of the mean-square error, stability and algorithm convergence.

Noise Removal from EMG Signal Using Adaptive Enhanced Squirrel Search Algorithm

2020 ◽  
Vol 19 (04) ◽  
pp. 2050039
Author(s):  
B. Nagasirisha ◽  
V. V. K. D. V. Prasad
Keyword(s):  
Adaptive Filters ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Maximum Error ◽  
Noise Removal ◽  
Least Square ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Emg Signal

Electromyogram (EMG) signals are mostly affected by a large number of artifacts. Most commonly affecting artifacts are power line interference (PLW), baseline noise and ECG noise. This work focuses on a novel attenuation noise removal strategy which is concentrated on adaptive filtering concepts. In this paper, an enhanced squirrel search (ESS) algorithm is applied to remove noise using adaptive filters. The noise eliminating filters namely adaptive least mean square (LMS) filter and adaptive recursive least square (RLS) filters are designed, which is correlated with an ESS. This novel algorithm yields better performance than other existing algorithms. Here the performances are measured in terms of signal-to-noise ratio (SNR) in decibel, maximum error (ME), mean square error (MSE), standard deviation, simulation time and mean value difference. The proposed work has been implemented at the MATLAB simulation platform. Testing of their noise attenuation capability is also validated with different evolutionary algorithms namely squirrel search, particle swarm optimization (PSO), artificial bee colony (ABC), firefly, ant colony optimization (ACO) and cuckoo search (CS). The proposed work eliminates the noises and provides noise-free EMG signal at the output which is highly efficient when compared with existing methodologies. Our proposed work achieves 4%, 40%, 4%, 7%, 9% and 70% better performance than the literature mentioned in the results.

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