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.

Self-Interference Cancellation in Full Duplex Communication using Steepest Descent Method

2021 ◽  
Vol 9 (VI) ◽  
pp. 1433-1442
Author(s):  
N. Alivelu Manga
Keyword(s):  
Interference Cancellation ◽  
Descent Method ◽  
Steepest Descent ◽  
Full Duplex ◽  
Steepest Descent Method ◽  
Interference Signal ◽  
Least Mean Square ◽  
Echo Canceller ◽  
Step Size ◽  
Signal Processing Technique

The present-day communication system uses Frequency Division Duplex (FDD) to emulate the benefits of Full Duplex Communication. But it requires more bandwidth as the cost of the spectrum is very high it becomes a major limitation. To overcome this problem implementation of Full Duplex Communication is the best solution. Implementation of full duplex communication is difficult because of a significant problem called self-interference. while transmitting and receiving signals on the same frequency band, receiving signal is interfered with the transmitted signal this phenomenon is called self-interference. The objective of this project is to minimize that self-interference signal from the received signal by using signal processing technique, LMS echo cancellation. Least Mean Square (LMS) echo canceller whose coefficients are updated iteratively is used to cancel the self-interference. An algorithm based on steepest descent method is used to obtain coefficients that change iteratively with varying step size to solve Weiner-Hopfs equation.

An Improved Variable Step Size LMS Algorithm Based on Sigmoid Function

2014 ◽  
Vol 602-605 ◽  
pp. 3593-3596 ◽  
Author(s):  
Li Liu ◽  
Qiong Wang ◽  
Hong Ping Bai
Keyword(s):  
Lms Algorithm ◽  
Variable Step Size ◽  
Sigmoid Function ◽  
Least Mean Square ◽  
Error Performance ◽  
Step Size ◽  
Channel Tracking ◽  
Channel Characteristics ◽  
Variable Step ◽  
Improved Algorithm

LMS (Least Mean Square) algorithm performance is analyzed. Then something has been done on SVSLMS that is an improved variable step size LMS algorithm based on sigmoid function. The improved algorithm has faster convergence rate, stronger channel tracking capability, better anti-noise performance and steady-state error performance compared with SVSLMS, for it makes parameters and change with channel characteristics. Theoretical analysis is the same as computer simulation results, which proves that the performance of the improved algorithm is superior to SVSLMS algorithm.

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.

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.

Application of Improved LMS Adaptive Algorithm in Acoustics Pyrometry

2014 ◽  
Vol 672-674 ◽  
pp. 2025-2028
Author(s):  
Shi Ping Zhang ◽  
Guo Qing Shen ◽  
Lian Suo An
Keyword(s):  
Temperature Measurement ◽  
Discrete Cosine Transform ◽  
Time Delay Estimation ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Boiler Furnace ◽  
Cosine Transform ◽  
Variable Step

Acoustic pyrometry is a comparatively advanced method of temperature measurement developed in recent years, which possesses the essential characteristics of traditional temperature measurement approach. Considering the interferences, like strong background noise, reverberation and so on, in boiler furnace, the LMS (least mean square) adaptive filter algorithm should be improved to meet certain environment above. In order to make the LMS algorithm have the characteristic of fast convergence and small steady state error, an improved, power-normalized and variable step-size discrete cosine transform LMS algorithm is proposed, which combines the power-normalized discrete cosine transform LMS algorithm with the variable step size LMS algorithm that uses the sliding forgetting-weighted window. The time delay estimation simulation in the strong-noise environment verifies the improved DCT-MVSS LMS algorithm can achieve good performance.

A Novel Design and FPGA Implementation of Filters Adapted Using LMS Variants

2018 ◽  
Vol 27 (08) ◽  
pp. 1850125
Author(s):  
Sakshi ◽  
Ravi Kumar
Keyword(s):  
Interference Cancellation ◽  
Adaptive Filters ◽  
Hardware Implementation ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Silicon Area ◽  
Novel Structure ◽  
Wide Range ◽  
Field Programmable

Adaptive filters have wide range of applications in areas such as echo or interference cancellation, prediction and system identification. Due to high computational complexity of adaptive filters, their hardware implementation is not an easy task. However, it becomes essential in many cases where real-time execution is needed. This paper presents the design and hardware implementation of a variable step size 40 order adaptive filter for de-noising acoustic signals. To ensure an area efficient implementation, a novel structure is being proposed. The proposed structure eliminates the requirement of extra registers for storage of delayed inputs thereby reducing the silicon area. The structure is compared with direct-form and transposed-form structures by adapting the filter coefficients using four different variants of the least means square (LMS) algorithm. Subsequently, the filters are implemented on three different field programmable gate arrays (FPGAs) viz. Spartan 6, Virtex 6 and Virtex 7 to find out the best device family that can be used to implement an Adaptive noise canceller (ANC) by comparing speed, power and area utilization. The synthesis results clearly reveal that ANC designed using the proposed structure has resulted in a reduction in silicon area without incurring any significant overhead in terms of power or delay.

Adaptive Fourier linear combiner based on modified least mean Kurtosis algorithm for the processing of sinusoidal signals

2019 ◽  
pp. 014233121988917
Author(s):  
Engin Cemal Mengüç
Keyword(s):  
Cost Function ◽  
Design Procedure ◽  
Sinusoidal Signal ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Step Size ◽  
Hand Tremor ◽  
The Mean ◽  
Sinusoidal Signals ◽  
Weight Coefficients

This study introduces an adaptive Fourier linear combiner (FLC) based on a modified least mean kurtosis (LMK) algorithm in order to effectively process sinusoidal signals, which we call FLC-LMK algorithm. In the design procedure of the proposed FLC-LMK algorithm, the classical kurtosis-based cost function is first modified for only sinusoidal signal distributions instead of Gaussian. Then, the FLC-LMK algorithm is derived from the minimization of this cost function and thus updates the weight coefficients of the FLC structure so as to directly process sinusoidal signals. Moreover, in this study, the convergence in the mean of the proposed FLC-LMK algorithm is analysed in order to determine the lower and upper bounds of its step size parameter. The most important contributions of the use of the proposed algorithm in the FLC structure are that it increases the convergence rate, decreases the steady-state error level and also has a robust behaviour against sinusoidal signal distributions due to its modified cost function. The performance of the proposed FLC-LMK algorithm is evaluated on the synthetic and real-world pathological hand tremor data by comparing with that of the FLC based on the classical least mean square (LMS) (FLC-LMS) algorithm. The simulation results support the mentioned properties of the proposed FLC-LMK algorithm.

scholarly journals Spectrogram Image Analysis of Error Signals for Minimizing Impulse Noise

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jeakwan Kim ◽  
Yunseon Choi ◽  
Young-Sup Lee
Keyword(s):  
Image Analysis ◽  
Impulse Noise ◽  
Active Noise Control ◽  
Convergence Speed ◽  
New Method ◽  
Least Mean Square ◽  
Real Time Control ◽  
Step Size ◽  
Time Control ◽  
Analysis Of Error

This paper presents the theoretical and experimental study on the spectrogram image analysis of error signals for minimizing the impulse input noises in the active suppression of noise. Impulse inputs of some specific wave patterns as primary noises to a one-dimensional duct with the length of 1800 mm are shown. The convergence speed of the adaptive feedforward algorithm based on the least mean square approach was controlled by a normalized step size which was incorporated into the algorithm. The variations of the step size govern the stability as well as the convergence speed. Because of this reason, a normalized step size is introduced as a new method for the control of impulse noise. The spectrogram images which indicate the degree of the attenuation of the impulse input noises are considered to represent the attenuation with the new method. The algorithm is extensively investigated in both simulation and real-time control experiment. It is demonstrated that the suggested algorithm worked with a nice stability and performance against impulse noises. The results in this study can be used for practical active noise control systems.

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