Comparitive analysis of smart antenna array basis of beamforming schemes and algorithems A Review - PDF
The smart antenna array is a group of antennas in which the relative phases of the respective signals feeding the antennas are varied in such a way that the effective radiation pattern of the array is reinforced in a desired direction and suppressed in undesired directions. Smart antenna are the array with smart signal processing algorithms used to identify spatial signal signature such as the direction of arriving of the signal, and use it to calculate beam forming vector, to track and locate the antenna beam on the mobile/target. An array antenna may be used to point a fixed radiation pattern, or to scan rapidly in azimuth or elevation. This paper explains the architecture; evolution of smart antenna differs from the basic format of antenna. The paper further discusses different Beamforming schemes and algorithms of smart antenna array.
(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, May 2010 Comparitive Analysis of Beamforming Schemes And Algorithems of Smart Antenna Array : A Review Abhishek Rawat , R. N. Yadav and S. C. Shrivastava Maulana Azad National Institute Of Technology Bhopal, INDIA Abstract— The smart antenna array is a group of antennas in which the relative phases of the respective signals feeding the antennas are varied in such a way that the effective radiation pattern of the array is reinforced in a desired direction and suppressed in undesired directions. Smart antenna are the array with smart signal processing algorithms used to identify spatial signal signature such as the direction of arriving of the signal, and use it to calculate beam forming vector, to track and locate the antenna beam on the mobile/target. An array antenna may be used to point a fixed radiation pattern, or to scan rapidly in azimuth or elevation. This paper explains the architecture; evolution of smart antenna differs from the basic format of antenna. The paper further discusses different Beamforming schemes and algorithms of smart antenna array. I. INTRODUCTION In the past, wireless communication systems are deployed with fixed antenna system with fixed beam pattern. Such configuration can not meet all the requirements of modern communication environments. Smart antennas - are the Fig. 1. Principle of smart antenna. technology that use a fix set of antenna elements in an array. The signals from these antenna elements are combined to form characteristics (such as a known alphabet or constant a movable beam pattern that can be steered to the direction of envelope) that the transmitted signal is known to have. The the desired user. This characteristic makes the smart antenna base station antennas have up till now been omni directional and minimizes the impact of noise, interference, and other or sectored. This can be regarded as a "waste" of power as effects that degrade the signal quality. The adoption of smart most of it will be radiated in other directions than toward the antenna techniques in future wireless systems is expected to user and the other users will experience the power radiated in have a significant impact on the efficient use of the spectrum, other directions as interference . The idea of smart the minimization of the cost of establishing new wireless antennas is to use base station antenna patterns that are not networks, the optimization of service quality, and realization fixed, but adapt to the current radio conditions. This can be of transparent operation across multi technology wireless visualized as the antenna directing a beam toward the networks -. Smart antenna systems consist of multiple communication partner only. antenna elements at the transmitting and/or receiving side of the communication link, whose signals are processed adaptively in order to exploit the spatial dimension of the II. TYPES AND GEOMETRY OF SMART ANTENNA mobile radio channel as shown in Fig.1. A smart antenna SYSTEMS receiver can decode the data from a smart antenna transmitter Smart antenna systems can improve link quality by this is the highest-performing configuration or it can simply combating the effects of multi-path propagation or provide array gain or diversity gain to the desired signals constructively exploiting the different paths, and increase transmitted from conventional transmitters and suppress the capacity by mitigating interference and allowing transmission interference. No manual placement of antennas is required. of different data streams from different antennas . Smart The smart antenna electronically adapts to the environment by antenna system technologies include intelligent antennas, looking for pilot tones or beacons or by recovering certain 123 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, May 2010 Fig.2 Different array geometries for smart antennas Fig.3. Comparison between three basic types of smart antenna. a) Uniform linear array b) Circular array; c) 2-Dimensional grid array d) 3-Dimensional grid array the system continuously updates beam selection, ensuring TABLE I. COMPARISON BETWEEN THREE BASIC TYPE OF SMART that user gets optimal quality for their call. The system scans ANTENNA. the outputs of each beam and selects the beam with the largest S. Switched Lobe Dynamically Adaptive output power as well as suppresses interference arriving from No Phased Array Array directions away from the active beam’s center. 1. A finite number It has fixed An infinite number The dynamically phased array smart antenna is an antenna of fixed, predefined number of array of patterns which controls its own pattern by means of feed-back or feed- patterns or which can be (scenario-based) that forward control, and it performs gain enhancement for desired combining strategies electronically are adjusted in real signals whereas suppression for interfering signals The phased (sectors) steered in a time. particular array antenna consists of multiple stationary antenna direction. elements, which are fed coherently and use variable phase or time delay control at each element to scan a beam to given 2. This kind of Easy to Complex in antenna will be move nature at the time angle in space. Array can be used in place of fix aperture easier to implement electronically. In of installment and antennas(reflectors , lenses ), because the multiplicity of in existing cell this case, the best performance elements allows more precise control of radiation pattern, thus structures than the received power is in the three types resulting in lower side band and careful pattern shaping . more sophisticated maximized. of smart antennas. adaptive arrays, The adaptive array system required sophisticated signal which also means processing algorithm to distinguish between desired signal , low cost. multipath signal and interference signal. It combine adaptive 3. The signal It does not Excellent digital signal processing to the spatial signal processing to strength can degrade null the performance in achieve greater performance. rapidly during the interference. interference. beam switching. III. BEAMFORMING SCHEMES OF SMART ANTENNA ARRAY phased array, digital beam forming, adaptive antenna systems, and others. Smart antenna systems are customarily The Beamforming scheme is important factor to categorized, however, as switched beam, dynamically phased convert antenna array into smart antenna. These schemes tilt array and adaptive array systems .Switched lobe creates a the radiation pattern into desired direction depending upon group of overlapping beams that together result in omni conditions. The simplest beamformer has all the weights of directional coverage. The overlapping beam patterns pointing equal magnitudes, and is called a conventional Beamformer in slightly different directions. The SBSA creates a number of or a delay-and sum beamformer. This array has unity two-way spatial channels on a single conventional channel in response in the look direction, which means that the mean frequency, time, or code. Each of these spatial channels has output power of the processor, due to a source in the look the interference rejection capabilities of the array, depending direction, is the same as the source power to steer the array in on side lobe level .As the mobile moves, beam-switching algorithms for each call determine when a particular beam a particular direction, the phases are selected appropriately. should be selected to maintain the highest quality signal and This beamformer provides the maximum output SNR for the 124 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, May 2010 case that no directional jammer operating at the same utilized by the constraint in the look direction. This may not frequency exists, but it is not effective in the presence of he true in a mobile-communications environment with multi- directional jammers, intentional or unintentional. Generally path arrivals, and the array Beamformer may not be able to null steering and optimal Beamformer are the commonly achieve the maximization of the output SNR by suppressing used in Smart antenna array . every interference. However, the Beamformer does not have to fully suppress interference, since an increase of a few A. Null-Steering Beamformer Null-steering beamforming techniques require not only control of phase (as for conventional beamforming), but also independent control of the amplitude. A null-steering Beam former can cancel a plane wave arriving from a known direction, producing a null in the response pattern in this direction. The process works well for canceling strong interference, and could he repeated for multiple-interference cancellation. But although it is easy to implement for signal interference, it becomes cumbersome as the number of interference grows. Although the beam pattern produce by this Beamformer has nulls in the directions of interference , it is not designed to minimize the uncorrelated noise at the array output. This can be achieved by selecting weights that minimize the mean output power, subject to the above constraints. The flexibility of array weighting to being adjusted to specify the array pattern is an important property. This may be exploited to cancel directional sources operating at the same frequency as that of the desired source, provided these are not in the direction of the desired source. In situations where the directions of these interferences are known, cancellation is possible by placing the nulls in the Fig 4 The structure of a narrow band beam-former (a)without pattern corresponding to these directions and simultaneously reference signal.and (b) using a reference signal. steering the main beam in the direction of the desired signal. Beam forming in this way, where nulls are placed in the decibels in the output SNR can make a large increase in directions of interferences, is normally known as null beam the channel capacity. In the optimization using reference forming or null steering. The cancellation of one interference signal method, the processor requires a reference signal by placing a null in the pattern uses one degree of the freedom instead of the desired signal direction (Fig.4). The array output of the array. Null beam forming uses the directions of sources is subtracted from an available reference signal to generate an toward which nulls are placed for estimating the required error signal, which is used to control the weights. Weights are weighting on each element. There are other schemes that do adjusted such that the mean squared error (MSE) between the not require directions of all sources. A constrained array output and the reference signal is minimized. Arrays Beamforming scheme uses the steering vector associated with which use zero reference signals are referred to as power- the desired signal and then estimates the weights by solving an inversion adaptive arrays. The MSE minimization sachem is a optimization problem. Knowledge of the steering vector closed-loop method, compared to the open –loop scheme of associated with the desired signal is required to protect the MVDR (the ML filter), and the increased SNR is achieved at signal from being canceled. In situations where the steering the cost of some signal distortion, caused by the filter. vector associated with the signal is not available, a reference signal is used for this purpose . IV. GENERALLY USED SMART ANTENNA B. Optimal Beamformer ALGORITHMS The optimal Beamformer referred also as the optimal combiner or minimum variance distortion less response beam At present, there are many sorts of algorithms that former (MVDR), does not require knowledge of the direction can be applied to the smart antenna systems. People also put and the power level of interference ,nor the level of the forward many modified algorithms on the basis of the basic background noise power , to maximize the output SNR. In this algorithms to adapt to different performance demands. case the weights are computed assuming all source as Generally, there are two categories: blind algorithm and non interference and processor is referred to as a noise along blind algorithm. The algorithm that needs the reference signal matrix inverse(NAMI) or maximum likelihood (ML) filter ,as to adjust the weights gradually is referred to as the blind it finds the ML estimate of the power of the signal source with algorithm. Besides, when the directions of the signals are the above assumption. Minimizing the total output noise, known, we can determine the channel response firstly, and while keeping the output signal constant, is the same as then determine the weights according to certain principle. maximizing the output SNR. This method requires the number This kind of algorithms includes LMS, RLS, SMI, LCMV of interferers to be less than or equal to L -2, as an array with and so on. Inversely, the blind algorithm doesn’t need the L elements has L- 1 degrees of freedom, and one has been reference signal. The receiver can estimate the transmitted 125 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, May 2010 signal and treat it as the reference signal to make signal processing. This kind of algorithm makes use of the inherent characteristics of the modulating signal or the characteristics that is independent of the carried information. This kind of (2) algorithms includes CMA, subspace algorithm, MUSIC algorithm, and so on. Moreover, the two kind of algorithm Where the inverse matrix is updated as can also be combined, namely, using the non blind algorithm to determine the initial value and then using the blind algorithm to track and adjust, such as SMI+CMA[l]. This method is suitable to the communication system that transmits the pilot symbols. (3) A. LMS Algorithm Where The LMS algorithm is based on the principle of the steepest descend and is applied to the MSE performance C. Sample Matrix Inversion (SMI) Algorithm measurement. The LMS algorithm intrudes three categories The SMI algorithm estimates the weights directly by  unconstrained LMS algorithm, normalized LMS estimating the covariance matrix R from K independent algorithm and constrained LMS algorithm. When the weights samples of data by time- averaging. Thus the problem that the are subjected to constraints at each iteration, the algorithm is rate of the convergence depends on the eigen value referred to as the constrained LMS algorithm. Otherwise, it is distribution can be avoided. The optimum solution obtained referred to as an unconstrained LMS algorithm. The from the SMI algorithm is[55 ]. unconstrained LMS algorithm is mostly applicable when weights are updated using a reference signal and no -1 knowledge of the direction of the signal is utilized. Though (4) the structure of the normal LMS algorithms are very simple, it doesn’t perform well due to its slow convergence rate in situation of fast-changing signal characteristics and the high H sensitivity to the eigen value distribution of the covariance Where matrix of the array signals, which limits its application in CDMA system. The normalized LMS algorithm is a variation i is a complex sample vector of receiver outputs of of the constant-step-size LMS algorithm, and uses a data- length N, N is the number of elements of the array antenna, K dependent step size at each iteration . is the number of sample vectors used. V is a steering vector of length N which is equal to the un adapted array weights. μ Forming a sample covariance matrix and solving for the μ ( n) = weights provides a very fast rate of convergence. The rate of ( n ) X ( n) H X convergence is dependent only on the number of elements and (1) is independent of the noise and interference environment and The algorithm normally has better convergence the eigen value distribution. Because the complexity of the performance and less signal sensitivity compared to the computing is proportional to N3 so it requires that the normal LMS algorithm. When applied to the multi-antenna algorithm has a strong processing ability when the array is CDMA mobile systems, using an optimal step-sequence in the large. To a certain given value of K, the quality of the update, the algorithm can achieve a fast convergence and a estimation obtained from the time average is dependent on the near-optimum steady-state performance at the expense of low input signal-noise ratio (SNR). When the SNR decreases, in increase in the complexity than the normal LMS order to eliminate noise and interference, a large amount of algorithm. Moreover, a modified and normalized. LMS samples are needed to obtain the estimation more precisely . (MN-LMS) algorithm is presented in . The adaptive filter Ronald L. etc had ever put forward the M-SMI algorithm, using this algorithm can track the individual total input phase namely the modified SMI, in which the diagonal loading at each element and the channel estimation and phase technique is used, where, the diagonal of the covariance calibration are not required for the inverse link improvement. matrix is augmented with a positive or negative constant prior to inversion. Compared to the SM1 algorithm, the diagonally B. RLS Algorithm loaded sample covariance matrix The RLS algorithm is based on the LS rule to make the error square-sum of the array output in each snapshot least . = (5) This algorithm take advantage of all the array data information that obtained after the initiation of the algorithm and using the F can be positive or negative, but for the covariance iteration method to realize the inverse operation of the matrix, matrix to be positive definite. The positive loading tends to so the convergence rate is rapid and can realize the tradeoff reduce the null depth on weak interfering signal, while it between the rate of the convergence and the computing decreases the convergence time. Conversely, negative loading complexity. This algorithm is not sensitive to the eigen value tends to increase the null depth on weak interfering signals distribution, but compared to the normal LMS algorithm, its while increasing convergence time. The SMI algorithm can computational complexity is high.The common solution get the maximum signal-to-interference-plus-noise (SINR). of the algorithm is However, in some applications, such as digital 126 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, May 2010 communications or satellite television communications, other above environment. The CMA algorithm can solve the measures of performance such as SIR may be equally problem . It is a typical blind algorithm and only requires important, the M-SMI can be applied in this situation. that the amplitude of the transmitted signal is constant, such as FM, PSK, FSK etc. CMA is based on the fact that the D. LCMV Algorithm amplitude of the combined signal fluctuates because of the The algorithms mentioned above all need the reference interference. Thus, in CMA. the amplitude of the combined signal, and the reference signal must have Large correlation signal is always observed, and the weights coefficients are with the desired signal. But in actual environment, this is adjusted so as to minimize the variation of the amplitude of difficult to obtain. So we can make use of the technology of the signal. When the output amplitude becomes constant, nulls orientation of the reference signal source. In the environment can be formed in the direction of the interference signals on that the signals are dense, we can orient the desired signal and the directional pattern. Moreover, Satoshi Denno etc have put the interference signal sources, and then combine this with the forward the Modified CMA algorithm in .The use of technology of nulling adaptively, thus we can obtain reference adaptive array to reject wideband interferences and track nulling with high resolution. It is assumed that there are p wideband signals has been proven to be more efficient if desired signals and q interference signals incident on the frequency compensation is used. Among the frequency antenna. The directions of the incident signals are ( θi ….., θp ) compensation algorithms, the interpolating techniques have and ( θp+1 …….., θp+q ) respectively in which p + q < M . The been applied to the CMA. ICMA permits to improve system constrained condition of the LCMV algorithm is: performances by readjusting the main lobe's direction toward the signal's DOA and increasing the interference null depth . V. FURTHER REMARKES (6) In this paper, we have discussed various Smart Where antenna array architectures, Beamforming techniques and algorithms. The design and architecture of smart antenna is case sensitive and changed according to the demand of applications. The adaptive array provide excellent result in the presence of interference, but its design is more complex and costly as compared of other two. In Beamforming null steering Beamforming perform well in case of strong This algorithm can ensure that the antenna has the gain of interferences, but in need prior information of that. The blind 1 in the directions of the desired signals, while the responses algorithm doesn’t need the reference signal so we can apply in the directions of the interference signals are zero, thus there them according the communication system demands. are deep nulls in the directions of the interference signals, which can be seen from the directional pattern of the antenna, REFERENCES Through these constrained conditions, the interference signals  Fang-Biau Ueng, Jun-Da Chen and Sheng-Han Cheng “Smart Antennas can be suppressed and the output power of the array can be for Multi-user DS/CDMA communications in Multipath Fading minimized to suppress other signals and noises which are not Channels” IEEE Eighth international symposium on spread spectrum ISSSTA2004, Sydney, Australia, 30 Aug. - 2 Sep. 2004 located in the main lobe of the antenna. The weight vector of the LCMV algorithm is:  Alexiou, A. , Haardt, M. “Smart antenna technologies for future wireless systems: trends and challenges “ IEEE Communications Magazine,Volume 42, Issue 9, Page(s):90 - 97 Sept. 2004  A. Rawat “Smart antenna terminal development” National conf. of (7) IETE Chandigadh, India April 2005 From above equation, we can see that in DS-CDMA  A. Rawat ”Design of smart antenna system for military application systems, the above two algorithms, namely SMI and LCMV using mat lab” National conf. of Institution of Engineers in Jaipur , India Aug 2006. algorithms, can be used by the adaptive antenna array for propagation delay estimation. The large sample maximum  Chryssomallis, M.” smarty antennas” Antennas and Propagation Magazine, IEEE Volume 42, Issue 3, Page(s):129 – 136 June 2000 likelihood (LSML) is applied to the beam forming output data  A. Paulraj, R. Nabar, and D. Gore, “Introduction to Space-Time for estimating to the propagation delay of a desired user in Wireless Communications”, Cambridge Univ. Press, 2003. multi-user sceneries. The adaptive antenna array can help the  L. C. Codara, “Application of Antcnna Arrays to Mobile LSML estimator to obtain improved performances as Communications, Part 11: Beam-Forming and Direclion-of-Arrival compared to a single antenna based LSML estimator. Considerations,” Proceedings of the IEEE, 85, pp.1195-1245, 8, August 1997 E. CMA Algorithm  Lal C. Godra, Application of Antenna Array to Mobile In order to adaptively control directions of nulls, some Communications, Part U : Beam-Forming and Direction-of-Arrival information concerning incident waves such as directions and Considerations”. Proceedings of the IEEE, Vol. 85, No. 8, Page(s): 1213-1218, 1997. intensity of incident waves is required. It is , however, very  Jian-Wu Zhang “The Adaptive Algorithms of the Smart Antenna difficult to know the information in some environment. In System in Future Mobile Telecommunication Systems” IEEE addition, the directions and intensity may vary with the International Workshop on Antenna Technology pp347-350, 2005 variation of the environment. Thus the algorithm for controlling the nulls is important especially in the case of 127 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 2, May 2010  Blair D. Carlson, “Covariance Matrix Estimation Erron and Diagonat  R. Kohno, C. Yim and H. Imai “Array Antenna Beamforming Based on Loading in Adaptive Arrays”. IEEE Transactions on Aerospace and Estimation on Arrival Angles Using DFT on Spatial Domain,” Electronic System. Vol. 24, No. 4, Page(s): 397-401, July 1988. Proceedings of PIMRC 1991, London, UK, ,pp. 38-43 September 1991.  Werner, S.; Apolinario, J.A.., Jr.; Lakkso, T.I. “Multiple-antenna  Jumarie, G. “Nonlinear filtering: A weighted mean squares approach CDMA Mobile Reception Using Constrained Normalized Adaptive and a Bayesian one via the maximum entropy principle.”Signal Algorithms”, Telecommunications Symposium, 199s. ITS ’98 Processing, 21 (1990), 323—338, 1990. Proceedings. SBT/IEEE International, Vol: 1, Page(s): 353-358 , 1998 .  Sun, Q., Alouani, A. T., Rice, T. R., and Gray, J. E. “ Linear system  Fujimoto, M.; Nishikawa, K.; Sato, K., “A Study of Adaptive Array state estimation: A neurocomputing approach.” In Proceedings of the Antenna System for Land Mobile Communications”, Intelligent American Control Conference, 550—554, 1992. Vehicles’95 Symposium, Proceedings of the IEEE, Page(s): 36-41, 25-  Cohen, S. A. “Adaptive variable update rate algorithm for tracking 26 Sept, 1995. targets with a phased array radar’. IEE Proceedings, pt. F, 133, 277—  Demo, S.; Ohira, T., “M-CMA for Digital Signal Processing Adaptive 280, 1986 . Antennas with Microwave Beamforming”,Proceedings of IEEE, Vol. 5,  J.C. Liberti, T.S. Rappaport, “Smart Antennas forWireless Page(s): 179-187 ,2000 . Communications: IS-95 and Third-Generation CDMA  Hefnawi, M.; Delisle, G.Y. “Adaptive arrays for wideband interference Applications”,Prentice Hall, NJ, 1999. suppression in wireless communications”, Antennas and Propagation  LAL C. GODAR4, Application of Antenna Array to Mobile Society, 1999. IEEE International Symposium 1999, vok3, Page(s): Communications, Part U : Beam-Forming and Direction-of-Arrival 1588 - 1591, 1999. Considerations”. Proceedings of the IEEE, Vol. 85, No. 8, Page(s):  Weijun Yao, and Yuanxun Ethan Wang, ”Beamforming for Phased 1213-1218, 1997. Arrays on Vibrating Apertures”, IEEE Trans. Antennas Propag., vol.  Sandgchoon Kim; Miller, S.L. “An Adaptive Antenna array Based 54,no.10, Oct. 2006 Propagation Delay Estimation for DS-CDMA Communication  A. H. El Zooghby, C. G. Christodoulou, and M. Georgiopoulos “Neural Systems”, Military Communications Conference, 1998. Milcom 98, Network-Based Adaptive Beamforming for One- and Two- Proceedings of the IEEE Vol: 1, Page(s):333-337, 1998 . Dimensional Antenna Arrays” IEEE Trans. Antennas Propag., vol. 46,  Sandgchoon Kim; Miller, S.L. “An Adaptive Antenna array Based no. 12 pp1891 -1893, Dec. 1998. Propagation Delay Estimation for DS-CDMA Communication  Hugh L. Southall,Jeffrey A. Simmers, and Teresa H. O’Donnell Systems”, Military Communications Conference, 1998. Milcom 98, “Direction Finding in Phased Arrays with a Neural Network Proceedings of the IEEE Vol: 1, Page(s):333-337, 1998 . Beamformer” IEEE Trans. Antennas Propag., vol. 43,no. 12 pp 1369-  BLAIR D. CARLSON, “Covariance Matrix Estimation Erron and 1374 , Dec1995. Diagonat Loading in Adaptive Arrays”. IEEE Transactions on  Robert J. Mailloux “Phased array antenna handbook” Artech Aerospace and Electronic System. Vol. 24, No. 4, Page(s): 397-401, House,2006 . July 1988.  Eric Charpentier, and Jean-Jacques Laurin, “An Implementation of a  Ronald L. Dilsavor, Randolph L. Moses, “Analysis of Modified SMI Direction-Finding Antenna for Mobile Communications Using a Neural method for adaptive Array Weight Control’, IEEE Transactions on Network” IEEE Trans. Antennas Propag., vol. 47, NO. 7pp 1152 -1158 Signal Processing, Vol. 41, No. 2, Page(s): 721-726,1993,. , JULY 1999.  Werner, S.; Apolinario, J.A.., Jr.; Lakkso, T.I. “Multiple-antenna  B. K. Yeo and Y. Lu, “Array failure correction with a genetic CDMA Mobile Reception Using Constrained Normalized Adaptive algorithm,”IEEE Trans. Antennas Propag., vol. 47, no. 5, pp. 823– Algorithms”, Telecommunications Symposium, 199s. ITS ’98 828,1999. Proceedings. SBT/IEEE International, Vol: 1, Page(s): 353-358 , 1998  M. Salazar-Palma, T. K. Sarkar, L.-E. G. Castillo, T. Roy, and A.  Fujimoto, M.; Nishikawa, K.; Sato, K., “A Study of Adaptive Array Djordjevic , Iterative and Self- Adaptive Finite-Elements in Antenna System for Land Mobile Communications”, Intelligent Electromagnetic Modeling. Norwood, MA: Artech House, 1998. Vehicles’95 Symposium, Proceedings of the IEEE, Page(s): 36-41, 25-  Amalendu Patnaik, B. Choudhury, P. Pradhan, R. K. Mishra, and 26 Sept, 1995. Christos Christodoulou “An ANN Application for Fault Finding in  Demo, S.; Ohira Demo, S.; Ohira, T., “M-CMA for Digital Signal Antenna Arrays “ IEEE Trans. Antennas Propag., vol. 55, no.3pp 775- Processing Adaptive Antennas with Microwave 777, Mar. 2007. Beamforming”,Proceedings of IEEE, Vol. 5, Page(s): 179-187 ,2000 .  R. F. Harrington, “Field Computation by Moment Methods”. New  Hefnawi, M.; Delisle, G.Y. “Adaptive arrays for wideband interference York:IEEE Press, 1993. suppression in wireless communications”, Antennas and Propagation  L. C. Codara, “Application of Antcnna Arrays to Mobile Society, 1999. IEEE International Symposium 1999, vok3, Page(s): Communications, Part 11: Beam-Forming and Direclion-of-Arrival 1588 - 1591, 1999. Considerations,” Proceedings of the IEEE, 85, 8, pp. 1195-1245, August 1997  M. Nagatsuka, N. Ishii, R. Kohno and H. Imai, “Adaptive Array Antcnna Based on Spatial Spcctral Estimation Using Maximum Enlrapy Method,” IEICE Trnnsactioris on Corn,rru,ricutiorrs,E77-B, 5, pp. 624-633, 1994. 128 http://sites.google.com/site/ijcsis/ ISSN 1947-5500