scholarly journals A Novel Dual Mode Decision Directed Multimodulus Algorithm (DM-DD-MMA) for Blind Adaptive Equalization

2021 ◽  
Author(s):  
Sagi Tadmor ◽  
Sapir Carmi ◽  
Monika Pinchas
Keyword(s):  
Power Input ◽  
Adaptive Equalization ◽  
Size Parameter ◽  
Constant Modulus Algorithm ◽  
Dual Mode ◽  
Step Size ◽  
Adaptive Equalizer ◽  
Blind Adaptive ◽  
Simulation Results ◽  
Blind Adaptive Equalization

In this paper, we propose for the 16 quadrature amplitude modulation (QAM) input case, a dual-mode (DM), decision directed (DD) multimodulus algorithm (MMA) algorithm for blind adaptive equalization which we name as DM-DD-MMA. In this new proposed algorithm, the MMA method is switched to the DD algorithm, based on a previously obtained expression for the step-size parameter valid at the convergence state of the blind adaptive equalizer, that depends on the channel power, input signal statistics and on the properties of the chosen algorithm. Simulation results show that improved equalization performance is obtained for the 16 QAM input case compared with the DM-CMA (where CMA is the constant modulus algorithm), DM-MCMA (where MCMA is the modified CMA) and MCMA-MDDMA (where MDDMA is the modified decision directed modulus algorithm).

Adaptive Equalization with Variable Step Size Algorithm Based on Gradient for HF Channel

2014 ◽  
Vol 989-994 ◽  
pp. 3702-3705
Author(s):  
Fang Fang Han ◽  
Zi Hao Lin
Keyword(s):  
Channel Model ◽  
Adaptive Equalization ◽  
Variable Step Size ◽  
Step Size ◽  
Performance Simulation ◽  
Channel Condition ◽  
Variable Step ◽  
Convergence Performance ◽  
Complex Channel ◽  
Hf Channel

To further improve the performance of adaptive equalization in HF channel, a new variable step size algorithm is proposed based on the iterative gradient. The step size is set to a vector according to the weights of the equalizer, and it can vary with the iterative gradient during the equalization process. The algorithm overcomes the traditional variable step size with the constant scale for all the weights of the equalizer, thus it can obtain faster convergence rate and lower steady state error in HF channel. In the complex channel condition, the algorithm proposed in this paper can avoid the local minimum point of the objective function to obtain the global convergence performance. Simulation result shows the effectiveness in the Watterson channel model.

scholarly journals The Tap-Length Associated with the Blind Adaptive Equalization/Deconvolution Problem

2020 ◽  
Vol 3 (1) ◽  
pp. 2
Author(s):  
Monika Pinchas
Keyword(s):  
Input Sequence ◽  
Point Of View ◽  
Adaptive Equalization ◽  
Size Parameter ◽  
Step Size ◽  
System Parameters ◽  
Large Step ◽  
Blind Adaptive ◽  
Deconvolution Problem ◽  
Blind Adaptive Equalization

The step-size parameter and the equalizer’s tap length are the system parameters in the blind adaptive equalization design. Choosing a large step-size parameter causes the equalizer to converge faster compared with applying a smaller value for the step size parameter. However, a higher step-size parameter leaves the system with a higher residual inter-symbol-interference (ISI) than does a lower step-size parameter. The equalizer’s tap length should be set large enough to compensate for the channel distortions. However, since the channel parameters are unknown, the required equalizer’s tap length is also unknown. The system parameters are usually designed via simulation trials, in such a way that the equalizer’s performance from the residual ISI point of view reaches a system desired residual ISI level. Recently, a closed-form approximated expression was derived for the residual ISI as a function of the system parameters, input sequence statistics and channel power. This expression was obtained under the assumption having a value for the equalizer’s tap length that is sufficient to compensate for the channel distortions. Based on this approximated expression, the outcome from the step-size parameter multiplied by the equalizer’s tap length can be derived when the residual ISI is given. By choosing a step-size parameter, we automatically have also the value for the equalizer’s tap length which might now not be large enough to compensate for the channel distortions and thus leaving the system with a higher residual ISI than the required one. In this work, we derive an expression that sets a condition on the equalizer’s tap length based on the input sequence statistics, on the chosen equalizer’s characteristics and required residual ISI. In addition, highlights are supplied on how to set the equalizer’s tap length for different channel cases based on this new derived expression. The findings are accompanied by simulation results.

A Variable Step-Size for Sparse Nonlinear Adaptive Filters

2021 ◽  
Author(s):  
Alberto Carini ◽  
Markus V. S. Lima ◽  
Hamed Yazdanpanah ◽  
Simone Orcioni ◽  
Stefania Cecchi
Keyword(s):  
Adaptive Filters ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

Sign in / Sign up

Export Citation Format

Share Document