scholarly journals Hybrid MVDR-LMS beamforming for Massive MIMO

2019 ◽  
Vol 16 (2) ◽  
pp. 715
Author(s):  
Yasmine M. Tabra ◽  
Bayan Sabbar
Keyword(s):  
Massive Mimo ◽  
High Speed ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Training Time ◽  
Multiple Users ◽  
Multiple Beams ◽  
Time Required ◽  
Beamforming Algorithm

<p>With the high speed of communication in LTE-5G, fast beamforming techniques need to be adopted. The training time required to form and steer the main lobes toward 5G multiple users must be short. Least-Mean-Square (LMS) training time is not suitable to work with in LTE-5G, but it has a good performance in forming multiple beams to large number of users and producing nulls in the interference direction. In this paper, an optimized hybrid MVDR-LMS beamforming algorithm is proposed to reduce the time required to estimate the antenna’s weights. This optimization is made by the benefit of previously set weights calculated using MVDR algorithms. The performance of the proposed hybrid MVDR-LMS algorithm tested using MATLAB 2016a.</p>

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

Prediction of Rotating Machinery Vibration Using Balance Simulation With Measurement Uncertainty

1995 ◽  
Author(s):  
Rob P. Andrews
Keyword(s):  
Measurement Uncertainty ◽  
High Speed ◽  
Rotating Machinery ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Balance Performance ◽  
Element Analysis ◽  
Balance Test ◽  
Influence Coefficients ◽  
Test Process

Abstract Vibration predictions for rotating machinery with high-speed flexible rotors must account for the methods and limitations of the balance test process which determine the residual rotor unbalance. Vibration predictions based on finite element analysis (FEA) methods are highly dependent upon the assumed rotor unbalance amplitude and phase. The actual residual unbalance distribution depends upon the measured influence coefficients and the least-mean-square (LMS) algorithm used to calculate balance correction weights. Repeatability of the vibration measurements is a key factor in successful balancing. The vibration predictions described in this paper use estimates of final residual unbalance obtained by simulating the balance test process. The simulation uses FEA based influence coefficients, a test based measurement uncertainty (repeatability) model, and LMS balance weight calculations including the specified vibration target levels. The simulations can be used to predict the limit of balance performance of the machinery and to evaluate design options for impact on residual unbalance levels.

Convergence analysis of Bessel beamformer and its comparison with LMS in adaptive array system

2015 ◽  
Vol 34 (3) ◽  
pp. 952-961 ◽  
Author(s):  
M. Yasin ◽  
Pervez Akhtar
Keyword(s):  
Theoretical Model ◽  
Convergence Analysis ◽  
Digital Signal Processor ◽  
Wiener Filter ◽  
Digital Signal ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Content Type ◽  
Convergence Performance

Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important performance parameters, namely; convergence and mean square error. The proposed BLMS algorithm is implemented on digital signal processor along with antenna array to make it smart in wireless sensor networks. Design/methodology/approach – Convergence analysis is theoretically developed and verified through MatLab Software. Findings – Theoretical model is verified through simulation and its results are shown in the provided table. Originality/value – The theoretical model can obtain validation from well-known result of Wiener filter theory through principle of orthogonality.

An Improved ZA-LMS Algorithm for Sparse System Identification with Variable Sparsity

2014 ◽  
Vol 602-605 ◽  
pp. 2415-2419 ◽  
Author(s):  
Hui Luo ◽  
Yun Lin ◽  
Qing Xia
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse System ◽  
Sparse System Identification ◽  
Least Mean Square Algorithm ◽  
Improved Algorithm

The standard least mean square algorithm does not consider the sparsity of the impulse response,and the performs of the ZA-LMS algorithm deteriorates ,as the degree of system sparsity reduces or non-sparse . Concerning this issue ,the ZA-LMS algorithm is studied and modified in this paper to improve the performance of sparse system identification .The improved algorithm by modify the zero attraction term, which attracts the coefficients only in a certain range (the “inactive” taps), thus have a good performance when the sparsity decreases. The simulations demonstrate that the proposed algorithm significantly outperforms then the ZA-LMS with variable sparisity.

scholarly journals SPEECH RECOGNITION USING SOM AND ACTUATION VIA NETWORK IN MATLAB

2014 ◽  
pp. 191-194
Author(s):  
A. SUBASH CHANDAR ◽  
S. SURIYANARAYANAN ◽  
M. MANIKANDAN
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Acoustic Noise ◽  
Lms Algorithm ◽  
Prototype System ◽  
Mean Square ◽  
Least Mean Square ◽  
Self Organizing Maps ◽  
Voice Signal ◽  
The Voice

This paper proposes a method of Speech recognition using Self Organizing Maps (SOM) and actuation through network in Matlab. The different words spoken by the user at client end are captured and filtered using Least Mean Square (LMS) algorithm to remove the acoustic noise. FFT is taken for the filtered voice signal. The voice spectrum is recognized using trained SOM and appropriate label is sent to server PC. The client and the server communication are established using User Datagram Protocol (UDP). Microcontroller (AT89S52) is used to control the speed of the actuator depending upon the input it receives from the client. Real-time working of the prototype system has been verified with successful speech recognition, transmission, reception and actuation via network.

scholarly journals Performance Evaluation of OFDM System with Insufficient CP Using LMS Equalizer under Harsh Multipath Conditions

2019 ◽  
Vol 15 (2) ◽  
pp. 122-129
Author(s):  
Abolqassem Fakher ◽  
Falih Alnahwi ◽  
Majid Alwan
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
High Performance ◽  
Minimum Mean Square Error ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Ofdm System ◽  
Zero Forcing ◽  
Ofdm Signal ◽  
Multipath Signals

This paper presents an insufficient cyclic prefix (CP) Orthogonal Frequency Division Multiplexing (OFDM) system with equalizer whose coefficients are calculated using Least Mean Square (LMS) algorithm. The OFDM signal is passed through a channel with four multipath signals which cause the OFDM signal to be under high inter-symbol interference (ISI) and inter-carrier interference (ICI).8-QAM and 16-QAM digital modulation techniques are used to evaluate the performance of the proposed system. The simulation results have accentuated the high performance of the LMS equalizer via comparing its Bit Error Rate (BER) and constellation diagram with those of the Minimum Mean Square Error and Zero Forcing equalizers. Moreover, the results also reveal that the LMS equalizer provides BER performance close to that of the OFDM system with a hypothetical sufficient CP.

scholarly journals New feedforward filtered-x least mean square algorithm with variable step size for active vibration control

2018 ◽  
Vol 38 (1) ◽  
pp. 187-198 ◽  
Author(s):  
Yubin Fang ◽  
Xiaojin Zhu ◽  
Zhiyuan Gao ◽  
Jiaming Hu ◽  
Jian Wu
Keyword(s):  
Vibration Control ◽  
Active Vibration Control ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Large Step ◽  
Variable Step ◽  
Active Vibration

The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.

Fast Beamforming of Compact Array Antenna

2009 ◽  
pp. 183-200
Author(s):  
Chen Sun ◽  
Takashi Ohira ◽  
Makoto Taromaru ◽  
Nemai Chandra Karmakar ◽  
Akifumi Hirata
Keyword(s):  
Smart Antenna ◽  
Central Element ◽  
Array Antenna ◽  
Least Mean Square ◽  
Digital Beamforming ◽  
Training Time ◽  
Parasitic Array ◽  
Output Sinr ◽  
Beamforming Algorithm ◽  
Compact Array

In this chapter, we describe a compact array antenna. Beamforming is achieved by tuning the load reactances at parasitic elements surrounding the active central element. The existing beam forming algorithms for this reactively controlled parasitic array antennas require long training time. In comparison with these algorithms, a faster beamforming algorithm, based on simultaneous perturbation stochastic approximation (SPSA) theory with a maximum cross-correlation coefficient (MCCC) criterion, is proposed in this chapter. The simulation results validate the algorithm. In an environment where the signal-to-interference ratio (SIR) is 0 dB, the algorithm converges within 50 iterations and achieves an output SINR of 10 dB. With the fast beamforming ability and its low power consumption attribute, the antenna makes the mass deployment of smart antenna technologies practical. To give a comparison of the beamforming algorithm with one of the standard beamforming algorithms for a digital beamforming (DBF) antenna array, we compare the proposed algorithm with the least mean square (LMS) beamforming algorithm. Since the parasitic array antenna is in nature an analog antenna, it cannot suppress correlated interference. Here, we assume that the interferences are uncorrelated.

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