International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN IN –
INTERNATIONAL JOURNAL OF ADVA...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 64...
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20120130406017

  1. 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN IN – INTERNATIONAL JOURNAL OF ADVANCED RESEARCH 0976 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 4, Issue 6, September – October 2013, pp. 166-174 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2013): 5.8376 (Calculated by GISI) www.jifactor.com IJARET ©IAEME AN LMS ADAPTIVE ANTENNA ARRAY SMITA BANERJEE1 and VED VYAS DWIVEDI2 1 Research Scholar, School of Engineering, R K University, Rajkot, Gujarat, India Assistant Professor, Department of Electronics and Communication Engineering, V.V.P. Engineering College, Rajkot, Gujarat, India (Parent Institute) 2 Director / Pro Vice Chancellor, C U Shah University, Wadhwan City, Surendranagar, Gujarat, India ABSTRACT The importance of adaptive antenna array to track the satellite automatically is described in this paper. The adaptive antenna array adjusts their pattern automatically to signal environment. The desired signal reception is maintained by steering the main beam. The self-steering capability of an adaptive antenna array is achieved by implementing LMS (Least mean square) algorithm. It is done by changing the phase of the element in antenna array and angle of beam steering. MATLAB software has been used to obtain array patterns for different values of phases in antenna array for different values of beam steering angle. Keywords: Adaptive Communication. antenna array, Adaptive beamforming, LMS algorithm, Satellite I. INTRODUCTION An adaptive antenna array has been widely used in different areas [1-3]. Satellite Communication Systems are advantageous in providing communication services to huge regions especially where the sufficient infrastructure for communication may not be constructed. In most satellite communication systems, interference remains a problem for reliable reception of signals. Adaptive antenna array is better for satellite communication systems as it has the ability to track the satellite automatically [4-5]. Hence for this reason we use adaptive antenna array that automatically steer the beam in the direction of desired signal i.e. signal of interest (SOI). An adaptive antenna array combines the outputs of antenna elements but controls the directional gain of the antenna by adjusting both phase and amplitude of the signal at each individual element [6-7]. The combined relative amplitude and phase shift for each antenna is called a complex weight. These weights are calculated using different algorithms [8-13]. The weighted signals are 166
  2. 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME summed and the output is fed to a controller that adjusts the weights to satisfy an optimization criterion. Adaptive antennas have the ability of separating automatically the desired signal from the noise and the interference signals and continuously update the element weights to ensure that the best possible signal is delivered in the look direction. It not only directs maximum radiation in the direction of the desired mobile user but also introduces nulls at interfering directions while tracking the desired mobile user at the same time [14-15]. The adaptation achieved by multiplying the incoming signal with complex weights and then summing them together to obtain the desired radiation pattern as shown in figure 1. These weights are computed adaptively to adapt to the changes in the signal environment. An adaptive algorithm is then employed to minimize the error between the desired signal and the array output [16]. Figure.1. Structure of Adaptive Array The optimum weights can be estimated with LMS algorithm. It is the most common and popular adaptive signal processing techniques. Array processing involves manipulation of signals induced on various antenna elements. It is the simplest and robust algorithm used for continuous adaptation [14, 17]. In this paper, analysis of adaptive techniques LMS, is done through MATLAB simulation by varying different parameters like desired direction and interference direction. Different complex weights are obtained using this LMS beamforming algorithm. This paper presents 8-elements array with λ/2 inter-element spacing and the LMS (least mean square) algorithms for the interference rejection of adaptive array antennas. An Adaptive beamforming is achieved by implementing LMS algorithm for directing the main beam towards the desired source signals and generating complex weights which can be used for interference rejection. By combining the signals incident on the linear antenna array and by knowing their directions of arrival, a set of weights can be adjusted to optimize the radiation pattern. In this paper MATLAB software has been used to obtain array patterns for different values of phases of elements in antenna array and angle of beam steering by using LMS algorithm. The performance of beamforming algorithms has been studied by means of MATLAB simulation. II. ADAPTIVE BEAMFORMING USING LEAST MEAN SQUARE ALGORITHM Beamforming is the term used to describe the application of weights to the inputs of an array of antennas to focus the reception of the antenna array in a certain direction, called the look direction 167
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME or the main lobe. These effects are all achieved electronically and no physical movement of the receiving antennas is necessary. In adaptive beamforming, the radiation pattern of adaptive antenna is controlled through various adaptive algorithms. Adaptive algorithm dynamically optimizes the radiation pattern according to the changing electromagnetic environment. The antenna array pattern is optimized to have maximum possible gain in the direction of the desired signal and nulls in the direction of the interferers. The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 is an adaptive algorithm, which uses a steepest decent technique of gradient-based method. LMS algorithm operates with a prior knowledge of the direction of arrival and the spectrum of the signal but with no knowledge of the noise and interference in the channel. It uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error [17-18]. An Adaptive Beamforming using least mean square algorithm consists of multiple antennas, complex weights, the function of which is to amplify (or attenuate) and delay the signals from each antenna element and a summer to add all of the processed signals, in order to tune out the signals of interest as shown in figure 2. Hence it is sometimes referred to as spatial filtering, since some incoming signals from certain spatial directions are filtered out, while others are amplified [19]. Figure.2. LMS Adaptive beam forming network Consider a Uniform Linear Array (ULA) with N isotropic elements, which forms the integral part of the adaptive beamforming system as shown in the figure 2 [20-21]. The narrow band incident waves are defined as s(t). s(t ) = A exp(2π f c t + φ ) (1) Where A: Amplitude of signal, fc: carrier frequency, ɸ: phase difference between incident waves at successive elements=(2Π/λ) dsin(θ), d: distance between successive antenna phase centers in the array, θ: angle of arrival w.r.t normal As they reach the antenna elements, the waves are converted to electrical signals x(t) . We define the input signals as x0(t),x1(t),.........xN-1(t). 168
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME 1    exp − jkd sin(θ ) ( )      : xi (t ) =   s (t ) :     :    exp(− jk ( N − 1)d sin(θ )  xi (t ) = a(θ ) s(t ) (2) Where N is the number of antenna elements and a(θ) is the steering vector which controls the direction of antenna beam. For adaptive beamforming, each element output xi(t) is multiplied by the weights w0,w1,w2, ........wN-1 that modify phase and amplitude of the incoming signal accordingly. This weighted signal is summed to give output. Then output signal y(t) of the antenna array is given by, N −1 y (t ) = ∑ xi (t ) wi (3) n=0 y (t ) = [ w0 w1 1    exp − jkd sin(θ ) ( )      : .. .. wN −1 ]   s (t ) :     :    exp( − jk ( N − 1) d sin(θ )  y (t ) = wi xT (t ) (4) An adaptive algorithm is then employed to minimize the error e(t) between a desired signal d(t) and the array output y(t). The overall antenna pattern is continuously modified by adjusting weight vector. This is a classical Weiner filtering problem for which the solution can be iteratively found using the LMS algorithm. III. LMS ALGORITHM FORMULATION The output at time n, y (n) is given by a linear combination of the data at the N sensors can be expressed as [22-23]: w = [ w1………wN]H (6 ) y(n) = wHx(n) (7) x(n) = [ x1(n)…………………xN(n)] (8) where H denotes complex conjugate. The weighted signals are summed and the output is fed to adaptive beamformer that adjusts the weights to satisfy an optimization criterion. 169
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME The optimum weights can be estimated with LMS algorithm. The LMS algorithm is an MMSE weight adaptation algorithm that uses the steepest descent algorithm and gradient based approach. It is referred to as sample by sample techniques as the weight vectors are updated for each new sample. The algorithm recursively computes and updates the weight vector. Successive corrections to the weight vector in the direction of the negative of the gradient vector eventually lead to the MMSE between the beamformer output and the reference signal. At this point the weight vector assumes to be its optimum value. . The LMS algorithm avoids matrix inverse operation by using the instantaneous gradient vector ɸJ (n) to update the weight vector. Let w (n) denotes the value of the weight vector at time n. The update value of the weight vector at time n+1 is w (n+1) can be written as, 1 (9) w( n + 1) = w( n) + µ [ −∇J ( n) ] 2 where µ is the step size which controls the speed of convergence. Its value is usually between 0 and 1. An exact measurement of the instantaneous gradient vector is not possible since this would require a prior knowledge of both the covariance matrix R and the cross-correlation vector r. Instead, an instantaneous estimate of the gradient vector ɸJ (n )is used which is given by, ∇ J ( n ) = −2 r ( n ) + 2 R ( n ) w( n ) (10) Correlation matrix R(n) = x(n) xH(n) (11) Signal correlation vector r (n) = d*(n)x(n) (12) where and are the instantaneous estimates of R and r defined in Equation respectively. Substituting Equations (10), (11) and (12) into Equation (9), the weight vector can be found that w( n + 1) = w( n) + µ [ r (n) − R ( n) w( n) ] = w(n) + µ x( n)  d ∗ (n) − x H (n) w( n)    (13) = w(n) + µ x( n)e∗ (n) ∗ where e∗ ( n ) = d ( n ) − x H ( n ) w ( n ) = error signal The LMS algorithm is initiated with an arbitrary value w(0) for the weight vector at n=0. The successive corrections of the weight vector eventually leads to the minimum value of the mean squared error. Therefore the LMS algorithm can be summarized in following equations; Output, y ( n ) = wH ( n ) x ( n ) (14) Error, e ( n ) = d (15) ( n) − y ( n) Weight, w ( n + 1) = w ( n ) + µ e* ( n ) x ( n ) 170 (16)
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME IV. SIMULATION RESULTS & STUDY For simulation purposes a 4-element linear array at 6 GHz is used with its individual elements spaced at half-wavelength distance i.e; 5 and wave number of 0.2. The amplitude and phase of the initial pattern is determined by arbitrary choice of weights, w whereas the final pattern is determined by the strength of desired signal, the direction and strength of the interfering signal in the system. In this simulation three different angle of arrival of single user and double interferers is considered in LMS algorithm. Figure shows that one can track the user and the interfering signals at the same time. Maximizing output SINR and minimizing MSE are two commonly used design criteria for beamforming. mu is considered as 1/(real(trace(Rx))). It can be shown in the simulations how interfering signals can be suppressed by putting nulls as well as how the beam steering can be done towards the desired signal with the LMS algorithm. Figure 3 is the normalized array factor plot before applying LMS. The angle of arrival of desired and interfering signals are known from the array factor plot. They are 0°, 60° and 90°. Figure.3. Normalized Array Factor plot before applying LMS Three cases studied keeping different angle of arrival of user. The direction of the user are at 0°, 30° and -45° while those of interferers are at (60°, 90°), (0°, 90°)and (0°, 90°). Table shows the directivity and half power beamwidth (HPBW) calculated for uniform linear antenna array before and after LMS at the specific steering lobe. TABLE-1 Uniform Linear Antenna Array Main lobe Before LMS Directivity HPBW 3.9920 34.3775 Steering lobe at 0° (after LMS) 4.2180 26.3561 Sterring lobe at -45° (after LMS) 4.2451 40.1070 Steering lobe at 30° (after LMS) 3.9999 34.3775 171
  7. 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME The array factor plot in figure 4-6 shows that the LMS algorithm is able to iteratively update the weights to force deep nulls at the direction of the interferers and achieve maximum in the direction of the desired signal. Figure.4. Normalized Array Factor plot for LMS adaptive antenna array having θ=0° Figure.5. Normalized Array Factor plot for LMS adaptive antenna array having θ=30° Figure.6. Normalized Array Factor plot for LMS adaptive antenna array having θ=-45° 172
  8. 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME V. CONCLUSION In this paper non-blind adaptive beamforming algorithm LMS have been analyzed on a adaptive antenna system for three different cases. The performance of the algorithm is evaluated through radiation pattern. The results illustrate the fact the LMS algorithm is able to iteratively update the weights to force deep nulls at the direction of the interferers and achieve maximum in the direction of the desired signal. It can be summarized that in satellite communication system, beam of adaptive antenna array can be steered in the direction of user and can suppress the interference by LMS beamforming algorithm. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] Balanis CA. Antenna Theory: Analysis and Design. 3rd Ed. New York: John Willy & Sons Inc.; 2005. A.N. Jadhav, V. M. Mhalgi and D.D. Khumane, “Wireless communication using Adaptive Smart Antenna System”, International Journal of Engineering Research & Technology (IJERT), Vol. 1 Issue 3, May – 2012. Das, S., "Smart antenna design for wireless communication using adaptive beam-forming approach," TENCON 2008 - 2008 IEEE Region 10 Conference , vol., no., pp.1,5, 19-21 Nov. 2008 Keng Jin Lian, “Adaptive antenna arrays for satellite personal communication systems”, Master of science thesis, January 27, 1997, Virginia polytechnic institute and state university, Blacksburg. A.Canabal, R.P.Jedicka and A.G.Pino, “Multifunctional phased array antenna design for satellite tracking”, Elsveier Journal, Volume 57, Issue 12, December 2005, Pages 887–900. B. Widrow et. al. “Adaptive antenna systems,” Proc. IEEE, Vol. 55, No.12 Dec., 1967. S.P. Applebaum, Adaptive Arrays, tech. rep., Syracuse University Research Corporation, 1965. Reprinted in IEEE Transactions on Antennas and Propagation, 1976. Byung Goo Choi, Yong Wan Park, Jeong Hee Choi , "The Adaptive Least Mean Square Algorithm Using Several Step Size for Multiuser Detection”: Vehicular Technology Conference, 2000. IEEE-VTS Fall VTC 2000 52nd, vol 6, pp. 2822 - 2825, 2000. Raed M. Shubair, Mahmoud A. Al-Qutayri, and Jassim M. Samhan, “A Setup for the Evaluation of MUSIC and LMS Algorithms for a Smart Antenna System” Journal of Communications, Vol. 2, NO. 4, June 2007. R.M. Shubair and A. Al Merri, “Robust Algorithms for Direction Finding and Adaptive Beamforming: Performance and Optimization,” Proceedings of IEEE International Midwest Symposium on Circuits and Systems (MWSCAS’04), Hiroshima, Japan, July 25-28, 2004, Pages 589–592. T. B. Lavate, V. K. Kokate & A. M. Sapkal, “Performance Analysis of MUSIC and ESPRIT DOA Estimation Algorithms for Adaptive Array Smart Antenna in Mobile Communication” International Journal of Computer Netwoks (IJCN), Volume(2):Issue(3), 2010. K.J. Krizman, T.E. Biedka, and T.S. Rappaport, “Wireless position location: fundamentals, implementation strategies, and sources of error,” Proc. IEEE Vehicular Technology Conference, Vol 12 pp. 919-923, Phoenix, Ariz, USA, MAY 1997. Smita Banerjee & Dr. Ved Vyas Dwivedi, “Review of adaptive linear antenna array pattern optimization”, published in International Journal of Electronics and Communication Engineering (IJECE) ISSN 2278-991X, Vol. 2, Issue 1, page no. 25-42, Feb. 2013. 173
  9. 9. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME [14] Koteswara Rao.Thokala and Ch.Jaya Prakash, “Steering An Adaptive Antenna Array By LMS NLMS and BBNLMS Algorithms”, Global Journal of Advanced Engineering Technologies, Vol1-Issue3-2012. [15] E.M. Al Ardi, R.M. Shubair, and M.E. Al Mualla, “Performance Evaluation of Direction Finding Algorithms for Adaptive Antenna Arrays,” Proceedings of IEEE International Conference on Electronics, Circuits, and Systems (ICECS’03), Sharjah, UAE, December 1417, 2003, Volume 2, Pages 735–738. [16] Haji, I.A., Islam, M.R., Alam, A.H.M.Z., Khalifa, O.O., Khan, S., Abdullah, K.A., Yussuf, A.A. , "Design and optimization of linear array antenna based on the analysis of direction of arrival (DOA) estimation and beamforming algorithms," Computer and Communication Engineering (ICCCE), 2010 International Conference on , vol., no., pp.1-4, 11-12 May 2010. [17] S.C.Upadhyay P. M. Mainkar, “Adaptive Array Beamforming using LMS Algorithm”, International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 1, January- 2013. [18] Gargouri, L.; Ghayoula, R.; Fadlallah, N.; Gharsallah, A.; Rammal, M., "Steering an adaptive antenna array by LMS algorithm," 16th IEEE International Conference on Electronics, Circuits, and Systems, 2009. ICECS 2009., vol., no., pp.459,462, 13-16 Dec. 2009. [19] R. S. Kawitkar and D. G. Wakde, “Smart antenna array analysis using LMS algorithm”, IEEE Int. Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, pp. 370-374, 2005. [20] Sangeeta Kamboj and Dr. Ratna Dahiya, “Adaptive antenna array for Satellite Communication Systems”, Proceedings of the International Multi Conference of Engineers and Computer Scientists 2008, IMECS 2008, 19-21 March 2008, Hong Kong. [21] Ibrahim A. H. Adam, MD. Rafiqul Islam, Othman O. Khalifa and AHM Zahirul Alam. “Effects of Linear Array Antenna Parameters to the Performance of LMS Beamforming Algorithm” The 7th International Conference on Robotics, Vision, Signal Processing, & Power Applications (RoViSP 2009). [22] Shahera Hossain, Mohammad Tariqul Islam and Seiichi Serikawa, “Adaptive Beamforming Algorithms for Smart Antenna Systems” International Conference on Control, Automation and Systems 2008, Oct. 14-17, 2008 in Coex, Seoul, Korea [23] Khyati R.Zalawadia, Twinkle V. Doshi and Dr U D Dalal, “Interference Rejection Performance of Adaptive Beam Forming through LMS Algorithm”, Proceedings of Second International Conference on Signals, Systems & Automation (ICSSA-11), 24-25 January, EC Department, G H Patel College of Engineering & Technology, Gujarat, India. [24] Dr. H.V.Kumaraswamy and Vijay B.T, “Efficient Beamforming Algorithm for MIMO Multicast with Application Layer Coding”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 2, 2013, pp. 116 - 128, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [25] Dhaval R. Bhojani and Dr. Ved Vyas Dwivedi, “Novel Idea for Improving Video Codecs”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 2, 2013, pp. 301 - 307, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [26] Mohammed Salman Ullah Khan and Prof. F.I. Shaikh, “Suppression of Power Line Interference Correction of Baseline Wanders and Denoising Ecg Signal Based on Constrained Stability Least Mean Square Algorithm”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 3, 2013, pp. 185 - 192, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. 174

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