Improvement of convergence speed in the filtered-x LMS algorithm with correlated reference signals

1997 ◽  
Vol 80 (12) ◽  
pp. 20-28 ◽  
Author(s):  
Guoyue Chen ◽  
Masato Abe ◽  
Toshio Sone
Keyword(s):  
Convergence Speed ◽  
Lms Algorithm ◽  
Reference Signals

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.

A Modified Algorithm Based on Decoration VSSLMS

2013 ◽  
Vol 850-851 ◽  
pp. 913-917
Author(s):  
Yi Gang Sun ◽  
Zhong Rui Xue ◽  
Wei Xing Chen
Keyword(s):  
Steady State ◽  
Convergence Speed ◽  
Lms Algorithm ◽  
Forgetting Factor ◽  
New Model ◽  
Modified Algorithm ◽  
State Error

To improve the performance of LMS algorithm ,a modified algorithm is proposed .decoration is used and orthogonal vectors is applied to replace weigh of the filter ,forgetting factor is also introduced to control the step .A new model is built and experiment show that convergence speed is improved and steady-state error is reduced. The performance is improved.

scholarly journals A Novel Self-Interference Cancellation Method Using an Optimized LMS Algorithm in CCFD Systems for a 5G Communication Network

2019 ◽  
Vol 9 (16) ◽  
pp. 3308
Author(s):  
Zeng-You Sun ◽  
Yu-Jie Zhao
Keyword(s):  
Wireless Communication ◽  
Interference Cancellation ◽  
Convergence Speed ◽  
Lms Algorithm ◽  
Full Duplex ◽  
Time Varying ◽  
Least Mean Square ◽  
Step Size ◽  
Channel Tracking ◽  
Step Factor

The Co-frequency Co-time Full Duplex (CCFD) is a key concept in 5G wireless communication networks. The biggest challenge for CCFD wireless communication is the strong self-interference (SI) from near-end transceivers. Aiming at cancelling the SI of near-end transceivers in CCFD systems in the radio frequency (RF) domain, a novel time-varying Least Mean Square (LMS) adaptive filtering algorithm which is based on step-size parameters gradually decrease with time varying called the DTV-LMS algorithm is proposed in this paper. The proposed DTV-LMS algorithm in this paper establishes the non-linear relationship between step factor and the evolved arct-angent function, and using the relationship between the time parameter and error signal correlation value to coordinately control the step factor to be updated. This algorithm maintains a low computational complexity. Simultaneously, the DTV-LMS algorithm can also attain the ideal characteristics, including the interference cancellation ratio (ICR), convergence speed, and channel tracking, so that the SI signal in the RF domain of a full duplex system can be effectively cancelled. The analysis and simulation results show that the ICR in the RF domain of the proposed algorithm is higher than that in the compared algorithms and have a faster convergence speed. At the same time, the channel tracking capability has also been significantly enhanced in CCFD systems.

scholarly journals Variable step-size LMS adaptive filters for CDMA multiuser detection

2004 ◽  
Vol 17 (1) ◽  
pp. 21-32 ◽  
Author(s):  
Karen Egiazarian ◽  
Pauli Kuosmanen ◽  
Ciprian Bilcu
Keyword(s):  
Multiuser Detection ◽  
Adaptive Filters ◽  
Convergence Speed ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Trade Off ◽  
Variable Step ◽  
Code Division Multiple

Due to its simplicity the adaptive Least Mean Square (LMS) algorithm is widely used in Code-Division Multiple access (CDMA) detectors. However its convergence speed is highly dependent on the eigen value spread of the input covariance matrix. For highly correlated inputs the LMS algorithm has a slow convergence which require long training sequences and therefore low transmission speeds. Another drawback of the LMS is the trade-off between convergence speed and steady-state error since both are controlled by the same parameter, the step-size. In order to eliminate these drawbacks, the class of Variable Step-Size LMS (VSSLMS) algorithms was introduced. In this paper, we study the behavior of some algorithms belonging to the class of VSSLMS for training based multiuser detection in a CDMA system. We show that the proposed Complementary Pair Variable Step-Size LMS algorithms highly increase the speed of convergence while reducing the trade-off between the convergence speed and the output error.

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.

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