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1. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 Smart antenna for wi-max radio system Neha shrivastava1, Sudeep Baudha2, Bharti tiwari3 Department of Electronics & Communication, Gyan Ganga College of technology, Jabalpur (M.P) 1 firstname.lastname@example.org, 2 email@example.com ,firstname.lastname@example.org .ABSTRACT base station serves a certain region called the cell. Omni directional antennas are employed in baseIn simple words, smart antenna is such that it can stations since the early days of the mobile systems.sense its environment and can adjust its gain in Base stations equipped with an omni directionaldifferent directions accordingly. They provide a antenna are located at the centre of each cell and thesmart solution to the problem of communication transmitter radiates to every point inside the cell at atraffic overload i.e. they increase the traffic capacity.They also improve the QOS. RF spectrum is a limited specified frequency. Omni directional antennas areresource and is becoming crowded day by day due to more prone to capacity leakage of the cell and hencethe advent of new technologies. The sources of to reduce these leakage directional antennas, alsointerference are increasing as well and hence called as Sectored antennas, are used. More than oneinterference is becoming the limiting factor for directional antenna can be placed on a base station,wireless communication. Smart Antenna adapts its each pointing to a different direction so that it isradiation pattern in such a way that it steers its main possible to sectorize the cell and use differentbeam in the DOA (direction of arrival) of the desireduser signal and places null along the interference. It frequencies in each sector for capacityrefers to a system of antenna arrays with smart signal improvement.processing algorithms. Array processing involvesmanipulation of signals induced on various antenna The next generation wireless communicationelements. Its capabilities of steering nulls to reduce system faces a challenge of offering higher data ratescochannel interferences and pointing independent in the range of hundreds of megabits per secondbeams toward various mobiles, as well as its ability which gives rise to the demand of wider frequencyto provide estimates of directions of radiating bands, but as of now there is a limitation on thesources, make it attractive to mobile communications spectrum usage, which can be overcome by the use ofsystem designer. Array processing is expected to playan important role in fulfilling the increased demands smart antennas. The demand for the use of smartof various mobile communications services. This antennas for mobile communications is increasedpaper provides a comprehensive and detailed recently and the main purpose for applying smarttreatment beam-forming,adaptive algorithms to adjust antennas is feasibility for increasing in capacity andthe required weighting on antennas, direction-of- efficiency. The Smart antennas, when usedarrival estimation methods. appropriately, help in improving the system1.INTRODUCTION performance by increasing channel capacity and spectrum efficiency, extending range coverage, Mobile communication systems are one of steering multiple beams to track many mobiles, andthe emerging technologies in recent years. There are compensating electronically for aperture distortion.now more than 2.2 billion mobile phone subscribers They also reduce delay spread, multipath fading, co-worldwide (ITU 2006), over one over third of the channel interference, system complexity, bit errorhuman population. This rapid growth of demand of rate (BER). mobile communications, force the service providersto improve their capacity by employing new Although some the principles of smarttechnologies on their systems. Mobile antennas have been around for about forty years, newcommunication systems are cellular systems that and improved wireless applications (in our case it isemploy base stations to serve its subscribers. Each the WiMAX technology) demanding the smart 293 All Rights Reserved © 2012 IJARCET
ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012antenna technology are growing exponentially. In I focus on the development of the array processingorder to be extremely effective in the present algorithms for the applications of beamforming anddynamic and dispersive multipath environment, the DOA estimationadaptive array algorithms, which control the 3.SMART ANTENNAoperation of the smart antenna and which form thebasis of this emerging technology, have to be quite 3.1NEED OF SMART ANTENNAS:smart enough. In this project, WiMAX technology isused which is quite new and has various advantages Wireless communication systems, as opposed toover the wireless technologies used now days like the their wire line counterparts, pose some uniqueWi-Fi technology. WiMAX serves greater number of challenges:users and offers higher security and greater quality of ƒ the limited allocated spectrum results in a limit on capacityservice. WiMAX technology is designed to allow ƒ the radio propagation environment and themobile users to move through the network mobility of users give rise to signal fadingseamlessly, although, the data rates keep on changing and spreading in time, space and frequencywhen a user moves far away from the WiMAX tower ƒ the limited battery life at the mobile devicebut the adjacent tower will automatically connect poses power constraintswith the user and will ensure the signal is In addition, cellular wireless communicationuninterrupted and clear. The smart antenna systems have to cope with interference due to frequency reuse. Among these methods are multipletechnology will add on to the features of WiMAX as access schemes, channel coding and equalization andthe users will be able to enjoy the services even if smart antenna employment. Fig below summarizestwo or three users are sharing the same frequency the wireless communication systems impairmentsbecause the smart antennas will form beam only in that smart antennas are challenged to combat.the direction of the desired user thus nulling out otherusers and interferences. 2. LITERATURE REVIEWAmongst the most interesting topics of arrayprocessing techniques are beamforming and theestimation of the direction of arrival (DOA) ofsignals, which are widely used in areas that includeradar, sonar, acoustics, astronomy, seismology, andcommunications. Here, we focus on theirdevelopments in communications, more specifically, Fig 3.1 Wireless communicationwireless communications, for attenuating impairments that smart antennas have to combatinterference, improving estimation accuracy, andlocating the positions of the sources. To simplify the An antenna in a telecommunications systemdiscussion, we concentrate on uniform linear array is the port through which radio frequency (RF)(ULA), which composes of a number of identical energy is coupled from the transmitter to the outsideelements arranged in a single line with uniform world for transmission purposes, and in reverse, tospacing. The extension of this material to other array the receiver from the outside world for receptionconfigurations is fairly straightforward in most cases purposes. To date, antennas have been the most. neglected of all the components in personal A detailed treatment of various methods of communications systems. Yet, the manner in whichestimating the DOA‟s has been provided by including radio frequency energy is distributed into andthe description,limitation, and capability of each collected from space has a profound influence uponmethod and their performance comparison as well as the efficient use of spectrum, the cost of establishingtheir sensitivity to parameter perturbations. new personal communications networks and the service quality provided by those networks. The commercial adoption of smart antenna techniques is a 294 All Rights Reserved © 2012 IJARCET
ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012great promise to the solution of the aforementioned 1. Switched Beamwireless communications‟ impairments. 2. Adaptive Arrays3.2 BASIC IDEA OF SMART ANTENNA: 3.3(a)SWITCHEDBEAM SMARTANTENNA The basic idea on which smart antenna It is the simplest implementation of a smartsystems were developed is most often introduced antenna, in which a single transceiver is connected towith a simple intuitive example that correlates their the RF beamforming unit. The RF beamforming unit,operation with that of the human auditory system. A switches to one of the predefined set of beams,person is able to determine the Direction of Arrival according to the received signal power or minimum(DoA) of a sound by utilizing a three-stage bit error ratio. It forms multiple fixed beams withprocess. heightened sensitivity in particular directions. Such an antenna system detects signal strength, chooses from one of several predetermined fixed beams, and switches from one beam to another as the cellular phone moves throughout the sector as shown in the figure below where we can see that the antenna subdivides the sector into many narrow beams. Each beam can be treated as an individual sector serving an individual user or a group of users. The cellular area is divided into three sectors with 120 degrees angular width, with each sector served by six directional Fig 3.2 Person‟s ear act as acoustic sensors narrow beams. The spatially separated directional which determine the desired user similar to a smart beam leads to increase in the possible reuse of a antenna frequency channel by reducing potential interference and also increases the range. These antennas do not have a uniform gain in all directions but when One‟s ears act as acoustic sensors and compared to a conventional antenna system they have receive the signal. increased gain in preferred directions.  Because of the separation between the ears, each ear receives the signal with a different time delay. The human brain, a specialized signal processor, does a large number of calculations to correlate information and compute the location of the received sound. To better provide an insight of how a smartantenna system works, let us imagine two personscarrying on a conversation inside an isolated room asillustrated in Fig. aboveThe listener among the two persons is capable ofdetermining the location of the speaker as he moves Fig .3.3 Working of Switched beam arraysabout the room because the voice of the speakerarrives at each acoustic sensor, the ear, at a different The basic working mechanism that thetime. The human “signal processor,” the brain, switched beam antenna follows is that the antennacomputes the direction of the speaker from the time selects the beam from the received signal that givesdifferences or delays received by the two ears. the strongest received signal. By changing the phase differences of the signals used to feed the antenna3.3SMART ANTENNA TYPES: elements or received from them, the main beam can be driven in different directions throughout space. There are basically two types of smart Instead of shaping the directional antenna pattern, theantenna configurations which dynamically change switched-beam systems combine the outputs oftheir antenna pattern to mitigate interference and multiple antennas in such a way as to form narrowmultipath effects while increasing coverage and sectorized (directional) beams with more spatialrange. They are; 295 All Rights Reserved © 2012 IJARCET
ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012selectivity that can be achieved with conventional, can perform the following functions: first thesingle-element approaches. direction of arrival of all the incoming signals including the interfering signals and the multipath signals are estimated using the Direction of Arrival3.3(b) ADAPTIVE ARRAY SYSTEMS algorithms. Secondly, the desired user signal is From the previous discussion it was quite identified and separated from the rest of theapparent that switched beam systems offer limited unwanted incoming signals. Lastly a beam is steeredperformance enhancement when compared to in the direction of the desired signal and the user isconventional antenna systems in wireless tracked as he moves while placing nulls at interferingcommunication. However, greater performance signal directions by constantly updating the compleximprovements can be achieved by implementing weights. advanced signal processing techniques to process theinformation obtained by the antenna arrays. Unlike 4.MUSIC ALGORITHAMswitched beam systems, the adaptive array systemsare really smart because they are able to dynamically MUSIC is an acronym which stands forreact to the changing RF environment. They have a MUltiple SIgnal Classification. This algorithm wasmultitude of radiation patterns compared to fixed first proposed by Schmidt and is a relatively simplefinite patterns in switched beam systems to adapt to and efficient eigenstructure method of DOAthe ever changing RF environment. An Adaptive estimation. It has many variations and is perhaps thearray, like a switched beam system uses antenna most studied method in its class. MUSIC providesarrays but it is controlled by signal processing. This unbiased estimate of the number of incoming signals,signal processing steers the radiation beam towards a their angle of arrival and the strength of thedesired mobile user, follows the user as he moves, waveforms. MUSIC makes the assumption that theand at the same time minimizes interference arising noise in each channel is uncorrelated making thefrom other users by introducing nulls in their noise correlation matrix diagonal. The incidentdirections. This is illustrated in a simple diagram signals may be somewhat correlated creating a non-shown below in figure below: diagonal signal correlation matrix. In MUSIC algorithm it is required that the number of incoming signals must be known or one must find out the eigenvalues which in turn are used to determine the number of incoming signal. If the number of signals is D, the number of signal eigenvalues and eigenvectors is D, and the number of noise eigenvalues and eigenvectors is M− D (M is the number of array elements). This method estimates the noise subspace from the available samples. This can be done by either eigenvalue decomposition of the estimated array correlation matrix or singular value decomposition of the data matrix, with its columns Fig 3.4 Adaptive beamforming being the K snapshots or the array signal vectors. Because MUSIC exploits the noise eigenvector subspace, it is sometimes referred to as a subspace `The adaptive array antenna comes under method.phased arrays, where the term phased array means The array correlation matrix is assumingthat the direction of radiation of the main beam in an uncorrelated noise with equal variances.array depends upon the phase difference between theelements of the array. Therefore it is possible tocontinuously steer the main beam in any direction by We next find the eigenvalues andadjusting the progressive phase difference β between eigenvectors for . We choose the eigenvectorsthe elements. The same concept forms the basis inadaptive array systems in which the phase is adjusted associated with the smallest eigenvalues. For uncorrelated signals, the smallest eigenvalues areto achieve maximum radiation in the desireddirection. An adaptive array smart antenna system 296 All Rights Reserved © 2012 IJARCET
ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012equal to the variance of the noise. We can then designated angular area. The fixed beamformingconstruct the techniques used are the maximum signal toM× (M− D) dimensional subspace spanned by the interference ratio (MSIR), the Maximum likelihoodnoise eigenvectors such that method (ML) and the Minimum Variance method (MV). If the arrival angles are such that they don‟t change with time, the optimum array weights won‟t The noise subspace eigenvectors which wefound are orthogonal to the array steering vectors for need to be adjusted.the different angle of arrival (θ1, θ2, θ3,...., θD) which However, if the desired arrival angles change with time, it is necessary to devise anare contained in the signal subspace. Thisorthogonality function can be used in the Euclidean optimization scheme that operates dynamicallydistance which is given as; according to the changing environment so as to keep recalculating the optimum array weights. The receiver signal processing algorithm then must allowAs explained above the noise eigenvectors and the for the continuous adaptation to an ever-changingarray steering vectors are orthogonal to each other the electromagnetic environment. Thus, if the userEuclidean distance will take the value zero for each emitting the desired signal is continuously movingof the angle of arrival and if this distance expression due to which the angle of arrival is changing, thisis placed in the denominator it creates peaks at the user can be tracked and a continuous beam can beangle of arrival. This expression is called the MUSIC formed towards it by using one of the adaptivepseudospectrum and is given as. beamforming techniques. This is achieved by varying the weights of each of the sensors (antennas) used in the array. It basically uses the idea that, though the signals emanating from different transmitters occupy the same frequency channel, they still arrive from different directions. This spatial separation is exploited to separate the desired signal from the interfering signals. In adaptive beamforming the optimum weights are iteratively computed using complex algorithms based upon different criteria. 4.1 Least mean square Algorithm- 4.1MUSIC Pseudospectrum 4.2An Adaptive array system4.ADAPTIVE BEAM FORMING Assuming this adaptive beamforming problem setupThere are two types of beamforming approaches; one where the array input x(t) and the desired signal d(t)is fixed beamforming approach which was used if the are assumed to be real valued, zero mean randomangles of arrivals don‟t change with time i.e. the user processes. The filter tap weights w0, w1, ...., wN areemitting the desired signals is fixed and not moving. also assumed to be real valued. We recall that theAs explained in the previous chapters, this type of performance function ξ is a quadratic function of theadaptive technique actually does not steer or scan the filter tap weight vector w and it has a singlebeam in the direction of the desired signal. Switched minimum which can be obtained using the Weiner-beam employs an antenna array which radiates Hopf equation given as;several overlapping fixed beams covering a 297 All Rights Reserved © 2012 IJARCET
ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 Wopt = R-1p Instead of solving the equation directly, wecan use the iterative approach to find wopt startingwith an initial guess for wopt say w(0), then the following recursive equation may be usedto update the weight vector w(k) can be given as w(k+1) = w(k) – μ. ∇kξwhere, μ is the step size;and ∇kξ is the gradient vector ∇ξ at point w = w(k);The LMS algorithm is a stochastic implementation ofthe Steepest Descent algorithm. It simply replaces theperformance function ξ = E[e2(n)] by itsinstantaneous course estimate ξ(n) = e2(n). Thussubstituting it in the above equation and replacing theiteration k by time index n for real time case. 4.4 polar Plot of LMS algorithm w(n+1) = w(n) – μ. ∇e (n) 2 where, ∇e2(n) = -2e(n).x(n) Therefore, w(n+1) = w(n) + 2μ.e(n)x(n)This equation is referred to as the LMS recursion. Itgives a simple method for recursive adaptation of thefilter coefficients after arrival of every input sequencex(n) and its desired output sample d(n). Once theweights are adapted and they reach the optimumsolution, a beam is formed in that particular directionwhich is the desire user signal while a null is formedin the direction of the interferers. As shown in thesimulated results (Cartesian and Polar plot) below thedesired signal is at 50 degrees in the direction ofwhich the beam is formed. Fig 4.5 Convergence of LMS algorithm The eminent feature of the LMS algorithm which makes it popular is its simplicity. It requires 2N + 1multiplications i.e. N multiplications for calculating the output y(n), one to obtain (2μ)×e(n) and N multiplications for scalar by vector (2μ.e(n)×x(n)) and 2N additions. Another good feature of LMS algorithm is its robustness and stable performance against different signal conditions. LMS algorithm takes to reach the optimum solution i.e. the desired user signal. As shown in the figure, the algorithm 4.3 Cartesian Plot of LMS algorithm takes 70 iterations to match with the reference signal which is equal to half the period of the reference signal 298 All Rights Reserved © 2012 IJARCET
ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012REFERENCES:-.Salvatore Bellofiore, Constantine A. Balanis,,”smart antenna for mobile communication network“.Department of electrical engineering, First author-Neha Shrivastava(student)-telecommunication , research centre , IEEE antenna received the B.E degree from the gyan gangaand propogation magazine,vol.44,no-3,june-2002. institute of technology and sciences(rgpv) Jabalpur,and persuing mtech (digital Farhad Gh. Khodaei , Javad Nourinia , Changiz communication)from GGCT Jabalpur.Ghobadi , „adaptive beam forming algorithm withincreased speed and improved reliability for smartantenna‟ ,urmia university ,iran,24 march 2010.Awan, PH 2008, Implementation of Smart Second author-Asst.Professor sudeepAntenna System Using Genetic Algorithm and baudha received the B.Tech degree from Govt.Artificial Immune System, viewed April 2011, Engineering College, Ujjain,and M.Tech in microwave from Indian institute of Hyung Seok Kim ,Sooyoung Yang , An efficient technology(IIT)kharagpur.his area of research isMAP in IEEE 802.16/WiMAX , broadband wireless antenaa design.access systems,14 February 2007; received in revisedform 27 April 2007. GODARA, LC, Application of Antenna Arrays toMobile Communications, Part II: Beamforming andDirection-of-Arrival Considerations, Aug. 1997 , Third author-Bharti Tiwari(student)-IEEE paper, vol 85, p. 1195–1245. received the B.E degree from the shri ram institute of technology (rgpv) Jabalpur,and persuing mtech Lamare, LWARCD, A Novel Constrained (digital communication)from GGCT Jabalpur.Adaptive Algorithm Using the Conjugate GradientMethod for Smart Antennas, IEEE paper, vol 2007,pp. 2243-2247.. Boroujeny, B-, Adaptive Filters Theory andApllication., John Wiley and Sons, New York.. D. G. Manolakis, V. K. Ingle, and S. M. Kogon,Statistical and Adaptive Signal Processing,McGraw-Hill, 2005.J. Boccuzzi, Signal Processing for WirelessCommunications, McGraw-Hill, 2007.. Gross, FB, Smart Antennas For WirelessCommunication, McGraw-Hill..J. Li and P. Stoica, Robust adaptivebeamforming, John Wiley, Inc. Hoboken, NewJersey, 2006. 299 All Rights Reserved © 2012 IJARCET