 Open Access
 Total Downloads : 495
 Authors : Nitesh Mudgal, Mr. Pankaj Shukla, Dr. R. S. Meena
 Paper ID : IJERTV2IS4417
 Volume & Issue : Volume 02, Issue 04 (April 2013)
 Published (First Online): 13042013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Design Of Quantized Least Mean Square Adaptive Filter For Adaptive Noise Cancellation
Design of Quantized Least Mean Square Adaptive Filter for Adaptive Noise Cancellation
Nitesh Mudgal, Mr. Pankaj Shukla, Dr. R. S. Meena
Abstract an adaptive filter finds its application in adaptive noise cancellation where a signal corrupted from noise is ex tracted and signal free from noise using an adaption algorithm is obtained. There are various types of adaptation algorithms for FIR filters such as least mean square (LMS) and recursive least square (RLS). The performance of these algorithms can be compared according to three parameters which are convergence speed, misadjustment and tracking capability. Convergence speed is simply the number of iterations needed for the filter to converge to its optimum state for a specific desired signal and input signal. in adaption algorithm it is not necessary to have any a priori knowledge of signal or noise characteristics that corrupt the signal. This paper proposes method of noise cancellation using quantized version of least mean square algorithm which provide better result as compared to normal least mean square algorithm. This property makes the adaptive filter has an important application in noise cancellation.
Index Terms sign function; modified sign function; least Mean Square algorithmt ; QXLeast Mean Square Algorithm.
The adaptive filters are popular owing to its simplicity but even simpler approaches are required for many realtime ap plications. Reduction of the complexity of the Adaptive filter had received attention in the area of adaptive filter. [1]
A signal corrupted from noise is to be extracted find its applica tion in various application fields, such as digital communications, radar, sonar and biomedical engineering, so that uncorrupted sig nal can be used for signal processing. And less power is utilized by system. Suppressing information bearing signal from signal corrupted by a sinusoidal interference utilize a fixed notch filter tuned to the frequency of the interference in traditional method. But in this case a precise frequency of the interference is to be known, but when notch is required to be very sharp, then adap tive noise cancellation provide solution for extracting information bearing signal from corrupted signal.
the method for filtering a information bearing signal from noise corrupted signal uses a filter that filters the noise from corrupted signal and information bearing signal remain unchanged. a fixed or adaptive filter can be utilized for filtering process.
Adaptive filters have the capability to adjust their own parame ters automatically using an adaption algorithm. On the other hand, fixed filters design is based on prior knowledge of both the

Nitesh Mudgal is currently pursuing master degree program in digital communication engineering in University college of engineering kota, India.

Mr. Pankaj Shukla is Asso. Professor in Electronics Dept. in University
college of engineering kota,India.
information bearing signal and the noise.
In design of adaptive filter a priori knowledge of signal or noise characteristics is not required. In this paper we have used adap tive filter for noise cancellation using quantized least mean square algorithm. An application of noise cancellation for adap tive filter have highly advantageous in various fileld.It makes use of primary input supplies an information bearing signal and a that are uncorrelated with each other. Noise is the correlated ver sion of the sinusoidal interference supplied as the reference input. Primary input contains both the signal and noise. Reference input is filtered and subtracted from a primary input so noise is atte nuated or eliminated by subtracting the reference input from pri mary input.

In order to improve signalto noise ratio (SNR) for a system, adaptive filters find an application of adaptive noise cancellation where noise from the corrupted information bearing signal is ex tracted. This process is known as adaptive noise cancellation. An Adaptive Noise Cancellation is typically a dualinput, closed loop adaptive feedback system where two inputs are: the primary input signal and reference input.
Fig. 1 Block diagram for Quantized Adaptive Noise cancellation scheme
Block diagram for quantized adaptive noise cancellation scheme is shown in fig. 1. A signal source used to transmit signal that signal is corrupted by a noise. The combined signal and noise form the primary input to the quantized adaptive noise canceller. Input to the filter receives a noise, uncorrelated with the signal but correlated in some unknown way with the noise. Adaptive filter processes this noise input signal using QXLMS adaption algorithm and filtered to produce an output y(n) that is approx imated version of noise. Then the output of adaptive filter is sub tracted from the primary input which is combination of signal and noise to produce the system output. The overall system output is the output for Quantized adaptive noise canceller.
In the system shown in Fig. 1, FIR filter is used as a adaptive filter where the reference input (noise) is processed. This filter differs from a fixed filter in sense that this filter automatically adjusts its coefficients weight (impulse response) using an adap tion algorithm. In this paper filter uses QXLMS adaption algo rithm so that the error can be minimize by adjusting the filter coefficients.

The LMS algorithm is a widely used algorithm for adaptive filter ing. The algorithm is described by the
Following equations:
Fig. 2 : Modified Sign Function
+1, x (i)
M 1
y(n) wk (n) x(n i) (1)
mgsn .
n
0, <xn (i)<
i0
1,x (i)
n
e(n) d(n) y(n) . (2)
wk (n 1) wk (n) 2 e(n) x(n i) …. (3)
In these equations, the tap inputs
x(n), x(n 1), x(n 2),…………., x(n M 1) form the elements of the reference signal x(n) , where M 1 is the number of delay elements. d (n) denotes the primary input sig nal, e(n) denotes the error signal and constitutes the overall sys
tem output. wk (n) denotes the tap weight at the nth iteration. In
equation (3), the tap weights update in accordance with the esti mation error. And the scaling factor is the stepsize parame
ter. controls the stability and convergence speed of the LMS algorithm..[2]
The LMS algorithm is convergent in the mean square if and only if satisfies the condition: 0<<2/tapinput power
M 1
Where tapinput power = E[ u(n k) 2 ]
k 0

The Modified Sign Function is three level quantization scheme whose value is dependent on the value of and is given as,[3]
Where msgn{.} is the modified sign function defined as:[1]
It should be noted that the implementation of such an adap tive filter has potentially greater throughput because for those times when the tap input signal, xn (i) , is less than the speci
fied threshold, , then xn (i) will be equal to zero and no coef
ficient adaptation for the corresponding weight needs to be
performed. This means that some of the timeconsuming op erations in the weight update formula can be omitted, thereby leading to a reduction of the computational load on the pro cessor. Whether this potential can be realized depends on the architeture used in the processor and also in applications
For this three level Quantization Scheme, Adaptive LMS algo rithm can be written as, [4]
w(n 1) w(n) e(n)x(n)
Where x(n) is the three levels Quantized input signal,

MATLAB results for the normal LMS and Quantized LMS are shown below, in normal LMS, primary input is the combina tion of information bearing signal and sinusoidal interference, input to the adaptive filter is the correlated version of the si nusoidal interference
amplitude
amplitude
2
0
2
2
amplitude 0
2
Desired Signal
0 100 200 300 400 500 600 700 800 900 10
iteration
0 100 200 300 400 500 600 700 800 900 10
iteration
Desired Signal
Estimated Signal
tions. In this paper, Quantized LMS is used for noise cancellation which is better as compared as normal LMS algorithm and results are compared on MATLAB.
00
REFERENCES
amplitude
amplitude
50 100 200 300 400 500 600 700 800 900 1000
iterationn

H. Sadoghi Yazdi, M. Fathy, Car tracking by quantized input LMS, QX

0
0 100 200 300 400 500 600 700 800 900 10
iteration
0 100 200 300 400 500 600 700 800 900 10
iteration
5
5
amplitude 0
5
Estimated Signal
error Signal
LMS algorithm in traffic scenes, IEE Proc.Vis. Image Signal Process., Vol. 153, No. 1, February 2006,pp. 3745.
00 [2] Mamta M.Mahajan,S.S. Godbole,Design of Least Mean Square Algorithm for adaptive noise canceller, (ijaest) international journal of advanced engineering sciences and technologies vol no. 5, issue no. 2, 172 – 176
amplitude
amplitude
20 100 200 300 400 500 600 700 800 900 1000
iteration

Xiaochun Guan,Xiaojing Chen,Guichu Wu,QX LMS Adaptive FIR Filters For System Identification, College of Physics & Electronics Information
0
0 100 200 300 400 500 600 700 800 900 10
iteration
0 100 200 300 400 500 600 700 800 900 10
iteration
2
2
amplitude 0
2
0
Fig. 3 Noise cancellation using LMS
Fig. 3 Noise cancellation using LMS
amplitude
amplitude
2
0
error Signal
Engineering, Wenzhou University,Zhejia ng China, 2009 IEEE.

B.Widrow and S. D. Steams,Adaptive Signal Proce ssing,China
00 Machine Press, Beijing, May 2008.
100
200
300
400 500 600
700
800
900
1000
DeisteirreadtiSonignal
100
200
300
400 500 600
700
800
900
1000
100
200
300
400 500 600
700
800
900
1000
DeisteirreadtiSonignal
100
200
300
400 500 600
700
800
900
1000

Lei Zhang, Jin Bi and Lianying Guo, The Practical Textbook of MATLAB,Post & Telecom press, Beijing, December 2008

Jun, B.E., Park, D.J., and Kim, Y.W, Convergence analysis of signsign LMS algorithm for adaptive filters with correlated gaussian data, Proc. ICASSP95, vol. 2, 1995, pp.13801383.
2
0
DeisteirreatdionSignal
Estimated Signal
Estimated Signal
2
e
e
amplitude
amplitude
amplitud 5
0
0
–52
00 110000
220000
330000 440000
550000
iitteerraatitoionn
660000
770000
880000
990000
11000000
amplitude
amplitude
55
amplitude
00
5
EsetirmroraSteigdnaSl ignal
50 100 200 300 400 500 600 700 800 900 1000
0 100 200 300 400 iter5a0ti0on
iteration
error
600 700 800 900 1000
5
amplitude
0
5
0 100 200 300 400 500 600 700 800 900 1000
iteration
Fig.4 Noise cancellation using modified clipped QX
LMS
Adaptive noise cancelling is a method of adaptive filtering that can be applied whenever a suitable reference input is available. The principal advantages of the method are its adaptive capability, its low output noise, and its low signal distortion. The adaptive capability allows the processing of inputs whose properties are unknown. Output noise and signal distortion are generally lower than can be achieved with conventional optimal filter configure