International Journal of Scientific and Research Publications, Volume 2, Issue 5, May 2012 ISSN 2250-3153

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SMART ANTENNAS IT’S BEAM FORMING AND DOA SURAYA MUBEEN, DR.A.M.PRASAD, DR.A.JHANSI RANI

Abstract- Smart antenna technology has the potential to significantly increase the efficient use of the spectrum in wireless communication applications in comparison to the existing methods in use. Through intelligent control of the transmission and reception of signals, capacity and coverage in mobile wireless networks can be significantly improved. Smart antenna is one of the most promising technologies that will enable a higher capacity in wireless networks by effectively reducing multipath and co-channel interference [3], [4], [5], [6]. This is achieved by focusing the radiation only in the desired direction and adjusting itself to changing traffic conditions or signal environments. Smart antennas employ a set of radiating elements arranged in the form of an array. The signals from these elements are combined to form a movable or switchable beam pattern that follows the desired user. In a Smart antenna system the arrays by themselves are not smart, it is the digital signal processing that makes them smart. The process of combining the signals and then focusing the radiation in a particular direction is often referred to as digital beam forming [3], [4]. Index TermsPATTERN

DOA,

BEAMFORMING,

RADIATION

I. INTRODUCTION

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mart antenna systems were designed for use in military applications to suppress interfering or jamming signals from the enemy [15]. Since interference suppression was a feature in this system, this technology was borrowed to apply to personal wireless communications where interference was limiting the number of users that a network could handle. It is a major challenge to apply smart antenna technology to personal wireless communications since the traffic is denser. Also, the time available for complex computations is limited. However, the advent of powerful, low-cost, digital processing components and the development of software-based techniques has made smart antenna systems a practical reality for cellular communications systems.

Figure 1: Smart Antenna Block Diagram There are basically two approaches to implement antennas that dynamically change their antenna pattern to mitigate interference and multipath affects while increasing coverage and range. They are Switched beam & Adaptive Arrays. The Switched beam approach is simpler compared to the fully adaptive approach. It provides a considerable increase in network capacity when compared to traditional omni directional antenna systems or sector-based systems. In this approach, an antenna array generates overlapping beams that cover the surrounding area as shown in figure (1).

Figure 2: Beam formation for switched beam antenna system The Adaptive array system is the “smarter” of the two approaches. This system tracks the mobile user continuously by steering the main beam towards the user and at the same time www.ijsrp.org

International Journal of Scientific and Research Publications, Volume 2, Issue 5, May 2012 ISSN 2250-3153

forming nulls in the directions of the interfering signal as shown in figure 4.2. Like switched beam systems, they also incorporate arrays. Typically, the received signal from each of the spatially distributed antenna elements is multiplied by a weight. The weights are complex in nature and adjust the amplitude and phase. These signals are combined to yield the array output. These complex weights are computed by a complicated adaptive algorithm, which is pre-programmed into the digital signalprocessing unit that manages the signal radiated by the base station.

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scenario, the interference rejection capability of the adaptive system provides significantly more coverage than either the conventional or switched beam systems [4].

Figure 4 Adaptation procedures: (a) Calculation of the beam former weights (b) Beam formed antenna amplitude pattern to enhance SOI and suppress SNOIs.

Figure 3: Beam formation for adaptive array antenna system

Now, let us assume that a signal of interest and two cochannel interferers arrive at the base station of a communications system employing smart antennas. Fig. below illustrates the beam patterns that each configuration may form to adapt to this scenario. The switched-beam system is shown on the left while the adaptive system is shown on the right. The light lines indicate the signal of interest while the dark lines display the direction of the co-channel interfering signals. Both systems direct the lobe with the greatest intensity in the general direction of the signal of interest..

II. BEAMFORMING IN ADAPTIVE AND SWITCHED BEAM SYSTEMS Basically, there are two major configurations of smart antennas: Switched-Beam: A finite number of fixed, predefined patterns or combining strategies (sectors). Adaptive Array: A theoretically infinite number of patterns (scenario-based) that are adjusted in real time according to the spatial changes of SOIs and SNOIs. In the presence of a low level interference, both types of smart antennas provide significant gains over the conventional sectorized systems. However, when a high level interference is present, the interference rejection capability of the adaptive systems provides significantly more coverage than either the conventional or switched beam system [4]. Fig. below illustrates the relative coverage area for conventional sectorized, switchedbeam, and adaptive antenna systems. Both types of smart antenna systems provide significant gains over conventional sectorized systems. The low level of interference environment on the left represents a new wireless system with lower penetration levels. However the environment with a significant level of interference on the right represents either a wireless system with more users or one using more aggressive frequency reuse patterns. In this

Coverage patterns for switched beam and adaptive array antennas

Beam forming lobes and nulls that Switched-Beam (left) and Adaptive Array (right)

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International Journal of Scientific and Research Publications, Volume 2, Issue 5, May 2012 ISSN 2250-3153

The adaptive system chooses a more accurate placement, thus providing greater signal enhancement. Similarly, the interfering signals arrive at places of lower intensity outside the main lobe, but again the adaptive system places these signals at the lowest possible gain points. The adaptive array concept ideally ensures that the main signal receives maximum enhancement while the interfering signals receive maximum suppression.

III. SWITCHED-BEAM ANTENNAS A switched-beam system is the simplest smart antenna technique. It forms multiple fixed beams with 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 illustrated in Fig

Switched-Beam Coverage Pattern The switched-beam, which is based on a basic switching function, can select the beam that gives the strongest received signal. By changing the phase differences of the signals used to feed the antenna elements or received from them, the main beam can be driven in different directions throughout space. Instead of shaping the directional antenna pattern, the switched-beam systems combine the outputs of multiple antennas in such a way as to form narrow sectorized (directional) beams with more spatial selectivity that can be achieved with conventional, singleelement approaches. A more generalized to the Switched-Lobe concept is the Dynamical Phased Array (DPA). In this concept, a direction of arrival (DOA) algorithm is embedded in the system [2]. The DOA is first estimated and then different parameters in the system are adjusted in accordance with the desired steering angle. In this way the received power is maximized but with the trade-off of more complicated antenna designs. The elements used in these arrays must be connected to the sources and/or receivers by feed networks. One of the most widely-known multiple beam forming networks is the Butler matrix.

SCHEMATIC DIAGRAM OF A 4 × 4 BUTLER MATRIXES

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IV. ADAPTIVE ANTENNA APPROACH The adaptive antenna systems approach communication between a user and a base station in different way by adding the dimension of space. By adjusting to the RF environment as it changes (or the spatial origin of signals), adaptive antenna technology can dynamically alter the signal patterns to optimize the performance of the wireless system. Adaptive array systems [12] provide more degrees of freedom since they have the ability to adapt in real time the radiation pattern to the RF signal environment; in other words, they can direct the main beam toward the pilot signal or SOI while suppressing the antenna pattern in the direction of the interferers or SNOIs. To put it simply, adaptive array systems can customize an appropriate radiation pattern for each individual user. Fig. below illustrates the general idea of an adaptive antenna system. The adaptive concept is far superior to the performance of a switched-beam system, as it is shown in Fig. above. A functional block diagram of the digital signal processing part of an adaptive array antenna system is shown in Fig. below.

Figure 5: Adaptive array coverage: A representative depiction of a main lobe extending toward a user with nulls directed toward two co-channel interferers.

V. DOA ESTIMATION Eigen Structure DOA Methods The families of DOA estimation algorithms that depend on an Eigen decomposition of the array covariance matrix are so named the Eigen structure methods. These methods rely on the following properties of the array covariance matrix R: Where X is the data matrix whose rows are N samples from each element of the array, H denotes Hermitian Transpose. First, the space spanned by its eigenvectors can be partitioned into two subspaces, namely, the signal subspace and the noise subspace. Second, vectors that correspond to directional sources are used. 5.1. Spectral Estimation Methods DOA estimation methods that first compute a spatial spectrum, then estimate DOAs by local maxima of this spectrum are called the Spectral Estimation Methods [1]. Essentially these methods apply weights to each element in the array so as to steer the antenna pattern towards a known look direction. The received power is then estimated for a large number of look directions and the look directions with maximum received power are chosen as the DOAs. Variants of the spectral estimation methods differ by how the weights are calculated to steer the main beam. Methods that fall under this class of DOA estimators include the www.ijsrp.org

International Journal of Scientific and Research Publications, Volume 2, Issue 5, May 2012 ISSN 2250-3153

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Maximum Likelihood method, Bartlett Method and the Linear Prediction Method [1]. These methods are inherently simple, but suffer from lack of resolution. For this reason the high resolution Eigen structure methods are most often used. orthogonal to the noise subspace, and are contained in the signal subspace [1]. 5.2. SPATIAL FILTERING The ability of the smart antenna to use the spatial dimension is the key factor to achieving the performance gains of adaptive arrays. Once accurate estimates of the DOAs impinging on the array have been made, desired signals can be passed through to the demodulator to further enhance the accuracy while attenuating interfering signals. This process effectively changes the receive antenna pattern from Omni-directional to directional, which can increase the BER rate performance and leads to the concept of spatial division multiple access (SDMA). Delay and Sum Beam forming The delay and sum beam former is the simplest of all spatial filtering schemes. If a desired signal from a known DOA is chosen then the main beam of the antenna array can be steered towards this direction by simply multiplying each element by a complex weight, corresponding to a delay, so that when the signals are combined the signal from the desired direction at each element add completely in phase. Consider the case of an M element Uniform Linear Array (ULA), an array whose elements are placed in a straight line equidistant apart. If a desired signal impinges on the array at an angle θ then there will be a constant time delay of this signal across the array τ. If the analytical signal received at each element is then multiplied by the complex weighting factor: Here i corresponds to the i th antenna element, the main beam of the array will be pointed in the direction θ. In general a vector whose elements correspond to the above weight factors is called a steering vector [1]. Figure 2 shows a plot of an M = 4 element array antenna pattern (-90° to 90°) after delay and sum weights have been applied. Two signals are impinging on the array from 32° and 15° at an SNR of 10 dB, the DOA estimation is done using ESPRIT and the simulation is done in Matlab. The axis represents the relative gain of the receive pattern.

NULL STEERING BEAM FORMING The delay and sum beam former is attractive because of its simplicity and ease of implementation. The limiting factor in the overall performance of this method is that though it can steer its main beam it has no control over its side lobes. This is evident from figure 2 where a side lobe of the antenna pattern allows the interfering signal, although attenuated, to reach the receiver after the weights are applied. The solution to this problem is the null steering or pseudo inverse beam former. If is the steering vector associated with the desired signal of interest and vectors are the k steering vectors associated with k interfering signals on an M element array, then the desired weight vector w is the solution of the following set of simultaneous equations [1]: Figure 3 shows the antenna pattern of a four element uniform linear array (-90° to 90°) under the same conditions of figure 2. Notice that a deep null has been steered in the direction of the interference, while gain is maintained in the direction of the signal of interest. s0 = 1 :

si = 0 :i = 1,…, k

MVDR BEAM FORMING

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International Journal of Scientific and Research Publications, Volume 2, Issue 5, May 2012 ISSN 2250-3153

The null steering scheme described in the previous section maximizes the SIR but does not maximize the overall output SNIR, that is it does not minimize the total noise including interferences and uncorrelated noise. It has been shown in [4] that the solution vector to the following optimization problem will yield the weights that maximize the output SNIR. Minimize This is equivalent to minimizing the mean output power while maintaining gain equal to the number of antenna elements in the direction of the signal of interest. The solution is given as:

Figure 4 shows a simulation of a 4 element array antenna pattern (-90° to 90°) after MVDR (Minimum Variance Distortion less Response) weights have been applied. Two signals are impinging on the array from -32° and 15° at an SNR of 10 dB, the DOA estimation is done using ESPRIT.

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efficient and robust DOA and beam forming methods.

REFERENCES J. Y.-L. Chou, “An investigation on the impact of antenna array geometry on beam forming user capacity,” Master’s thesis, Queen’s University, Kingston, Ontario, Mar. 2002. [2] I. Stevanovi´c, A. Skrivervik, and J. R. Mosig, “Smart antenna systems for mobile communications,” Ecole Polytechnique F´ed´erale de Lausanne, Lausanne, Suisse, Tech. Rep., Jan. 2003. [Online]. Available: http://lemawww.epfl.ch [3] A. Paulraj, B. Ottersten, R. Roy, A. Swindle hurst, G. Xu, and T. Kailath, Sub space Methods for Direction of Arrival Estimation. Amsterdam: NorthHolland, 1993, vol. 10, ch. 16, pp. 693–739. [4] W. Y. Shiu, “Non iterative digital beam forming in CDMA cellular communications [5] Godara, L. C., Application of Antenna Arrays to Mobile Communications, Part II: Beam forming and Direction-of-Arrival Considerations, Proceedings of the IEEE, Vol. 85, No. 8. pp 1195- 1245, August 1997 [6] Okamoto G.T. Smart Antennas and Wireless LANS. Kluwer Academic Publishers, Norwell Mass., 2002 [7] Tsoulos G.V. Meach M.A. Swales S.C. Adaptive Antennas for Third Generation DS-CDMA Cellular Systems, Proceedings of the 45th Vehicular Technology Conference, Vol. 1, pp 45-49, July 1995 [8] “Special issue on adaptive antennas,” IEEE Trans. Antennas Propagation., vol. 24, no. 5, Sept. 1976. [9] “Special issue on adaptive processing antenna systems,” IEEE Trans. Antennas Propagation .,vol. 34, no. 3, Mar. 1986. [10] P. H. Lehne and M. Pettersen, “An overview of smart antenna technology for mobile communications systems,” IEEE Communications Surveys, vol. 2, no. 4, pp. [1]

AUTHORS First Author – SURAYA MUBEEN, KL UNIVERSITY, ASSITANT PROFESSOR ECE, [email protected] Second Author – DR.A.M.PRASAD, JNTU KAKINADA, ASSOCIATE PROFESSOR ECE VI. FUTURESCOPE Perhaps the reason why only a handful of companies are producing viable commercial smart antenna products is because their practical implementation is extremely difficult. DOA and beam forming algorithms require a large number of computations which makes it difficult for them to keep up with the high data rates of today’s wireless systems; this makes research into highly

Third Author – DR.A.JHANSI RANI, VRSEC VIJAYAWADA, PROFESSOR ECE

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