JOURNAL OF TELECOMMUNICATIONS, VOLUME 8, ISSUE 2, MAY 2011 45

Weights Optimization of 1D and 2D Adaptive Arrays Using Neural Network Approach Mohammed A. Ghali, Abdulkareem S. Abdullah Department of Electrical Engineering College of Engineering, University of Basrah

Abstract- In this paper, a neural network approach to the problem of finding the weights of one and two dimensional adaptive arrays is presented. In this approach, the computation of the optimum weights is accomplished using three-layer radial basis function neural networks (RBFNN), which treats the problem of computing the weights of an adaptive array antenna as a mapping problem. The algorithm is performed for two applications, linear and planar arrays at different distributions. Keywords- smart antenna, neural network, radial base function, adaptive beamforming

1 INTRODUCTION Neural Network (NN) algorithms have been more popular in signal processing applications. According to the improving mobile communication, Global Positioning System (GPS) and Radar technologies, faster beamforming algorithms are needed. Since the number of users and the interfering signals increases, the communication systems require to track the users continuously while they are moving, and to put nulls in the directions of interferences. Neural Networks have good performances in accordance of these needs, and can easily be implemented for these applications [1]. The main idea of NN applications is to define input and output pairs for the training phase. The inputs of the training phase have to be chosen carefully, since the NN is going to make an optimization for a new, unseen input according to the trained input and output pairs. In this paper, beamforming applications are applied with NN. The main idea of this beamformer is to direct the antenna array patterns to the desired signal directions and to put nulls in the directions of interferences. The inputs are chosen as the correlation matrices of the incoming signals from sources. The inputs have all the possibilities of the direction of arrival (DOA) information of ————————————————

Mohammed. A. Ghali and Abdulkareem S. Abdullah are both with the Department of Electrical Engineering, College of Engineering, University of Basrah, Basrah, IRAQ.

the incoming signals. The outputs are the weights of the antenna elements with respect to each correlation matrices. These weights are used to steer and shape the antenna pattern. Radial Basis Function Neural Network (RBFNN) is used for training phase. In section 2, brief information of RBFNN is described. Section 3 discusses adaptive beamforming with NN and drives its mathematical equations. Section 4 and 5 describes the training and performance phase of the NN. In Section 6, simulation results from computer are presented and discussed. Finally, our conclusions are given in Section 7.

2 RASIAL BASIS FUNCTION NEURAL NETWORK The construction of a RBF network in its most basic form involves three layers with entirely different roles [2]. The input layer is made up of source nodes (sensory units) that connect the network to its environment. The second layer, the only hidden layer in the network, applies a nonlinear transformation from the input space to the hidden space. In most applications the hidden space is of high dimensionality. The output layer is linear, supplying the response of the network to the activation pattern (signal) applied to the input layer. Since RBFNN network has fast learning speed and needs less iteration for converging to the target values, it is used for the beamforming applications of this work. The architecture of the RBFNN is shown in Fig.1.

© 2011 JOT http://sites.google.com/site/journaloftelecommunications/

JOURNAL OF TELECOMMUNICATIONS, VOLUME 8, ISSUE 2, MAY 2011

The command returns the corresponding output weights of given input correlation matrix.

Y  simnet, P

4

Fig. 2. Linear Array Geometry. Fig. 1. Radial basis function neural network.

The mapping between the input-hidden layers is nonlinear. There is a linear combination between hidden and output layers. The mapping function is expressed as:

        

1

where N is the number of functions,  are the centers of the radial basis function and,

  

  

2

where  is called as the transfer function of the neural network’s input-output pairs. In this work, two MATLAB commands of RBFNN are used for training and performance phases of the beamformer. 1-"newrbe” is the training MATLAB command of the RBFNN. The inputs of this command are the inputs, outputs and spread of the RBFNN. The inputs are the correlation matrices, and the outputs are the weights.

net  newrbeP, T, spread

3

The function newrbe takes matrices of input vectors P and target vectors T, and a spread constant SPREAD for the radial basis layer, and returns a network with weights and biases such that the outputs are exactly T when the inputs are P [3]. 2-"sim" is the performance MATLAB command. The inputs of this command are trained NN, input, network targets.

3 MATHMEATICAL MODEL Consider a linear array composed of M elements. Let K (K
   34 1 5 .6 7 8 1 49

0  1 ,2 ,…;

5

where 34 1 is the signal of the <12 wave, 8 1 is the noise signal received at the 012 sensor and

=4 

>? @ A

sin ,4

6

where C is the spacing of the elements of the array, and D is the speed of light in free space. Using vector notation we can write the array output in a matrix form

Xt   ASt 7 Nt

7

Xt  Ix t x t … xK t LM

8

where Xt, Nt, and St are given by Nt  In t n t … … nK t LM

St  Is t s t … … sP t

LM

9

10

In (7), R is the ; S = steering matrix of the array toward the direction of the incoming signals. T and U are I; S 1L - sized vectors. 3 is a I= S 1L sized vector

R  IV ,  V ,  . . . V,.  L

11

JOURNAL OF TELECOMMUNICATIONS, VOLUME 8, ISSUE 2, MAY 2011

where V,4  is the steering vector associated with direction ,4 :

V,4   X1  5.4  5.4 …  5Y .4 Z

[

12

After deriving the induced signal to the linear array, the correlation matrix of each incoming signal is calculated. As mentioned earlier, the correlation matrices are used for deriving the inputs of the NN for training and for the performance phases. The correlation function is derived from the induced signals on each array element, and given as

R  E+XtXt^ /

 AEIStS^ tLA^ 7 EINtN ^ tL

13

The first row of the correlation matrix is taken into account for calculating the Z vector. The derived Z vector is then given as the input of NN. The correlation function and vector- b are given as,

R

R  ` R 

R a

R  R R a

R a Ra b R aa

b  IR

R  R a R R a R aa L Z vector is obtained from b

Z

b

b

14

15

16

The elements of the correlation matrix R are complex values. Since the NN does not accept the complex values, real and imaginary parts of each element of the matrix is considered separately. The size of Z vector in (16) is IM S 1L. Resizing the vector, by separating it into its real and imaginary parts changes the size of Z to [2M S 1L. Z vector is given as the input to the RBFNN. The next step is to calculate the outputs of the NN. The outputs are the optimum weights of the linear array elements for corresponding DOA’s. The formulation of weight calculation is given in [1]. The weights minimize the signals received from interferences and maximize the array response for the desired signal directions. The optimum weights are calculated as:

def  g  3@ I3@ g  3@ L h

17

The steering matrix of the desired signals is expressed as:

3@  I3@ ,  3@ ,



3@ ,i L

18

The term h in the (17) is the characteristic parameter that determines if the signals are interfering or desired signals. If there are two desired signals, then h  I1 1L. If there is a single desired and a single interfering signal, then h  I1 0L.

4 GENERATION OF TRAINING DATA

45

Generate array output vectors +T j 1; l  1,2, … , U[ /. 2. Normalize each one of the above array output vectors by its norm [4]. For simplicity of notation we still refer to these vectors by m1’s. 3. Evaluate the correlation matrix gj l  1,2, … , U[  for each of the array output vectors generated in step 1. Using the calculated gj ’s, calculate the j vectors+ndef ; l  1,2, … , U[ / based on the Wiener solution. 4. Produce the required training input/output pairs j of the training set, that is +m j 1; ndef ;l  1,2, … , U[ /. 1.

5 PERFORMANCE PHASE OF THE RBFNN After the training phase is complete, the RBFNN has established an approximation of the desired input-output mapping. In the performance phase, the neural network is expected to generalize, that is, respond to inputs that has never seen before, but drawn from the same distribution as the inputs used in the training set. In the performance phase, the RBFNN produces outputs to previously unseen inputs by interpolating between the inputs used in the training phase [5]. 1. 2.

Generate the rearranged covariance matrix. Present the array output vector at the input layer of the trained RBFNN. The output layer of the trained RBFNN will produce the estimation of the weight vector for the array output

6 RESULT SIMULATIONS AND DISCUSSION The simulations are divided into two main groups: linear antenna array and planar antenna array applications. In linear array simulations, the performances of the beamformer are examined according to the number of array antenna elements, number of the interference signals and the type of the distribution.

6.1 Linear Array with Uniform Distribution A uniform linear antenna array with different number of antenna elements (N = 6, 9, 12) is adopted here. The distance between the antenna elements is taken to be d=λ/2 and the angular range of interest is I 90* , 90* L. 1.

2.

Desired signal at ,  30* and two interference signals at ,   30* , 60* . Desired signal at ,  30* and three interference signals at ,  60* , 0* , 60* .

© 2011 JOT http://sites.google.com/site/journaloftelecommunications/

JOURNAL OF TELECOMMUNICATIONS, VOLUME 8, ISSUE 2, MAY 2011

Fig.3 and Fig.4 show array factor patterns which have a peak at the desired direction while simultaneously make nulling at the directions of the interference signals. From these figures, it is evident that as number of antenna elements increases, the width of main lobe decreases, this is crucial for the application of smart antennas when single narrower beam is required to track the mobile.

6.2 Linear Array with Dolph- Chebyshev Distribution In nonuniform arrays of Dolph - Chebyshev distribution there is a compromise between half power beamwidth (HPBW) and directivity. This is demonstrated in Fig.5, Fig.6 with (N=6, 9, 15), and the angular range of interest is I 0* , 180* L. 1.

Fig. 4. Adapted pattern of linear array for desired signal and two interfering signals.

Desired signal at ,  90* and interference signal at ,  45* with SLR=20.

Desired signal at ,  90* and interference signal at ,  120 with SLR=30. It is shown that the resultant array factor has a peak at the 2.

desired direction while simultaneously make nulling at the directions of the interference signal. As it is expected from the antenna array theory, increasing the number of antenna elements, the beamwidth of the antenna array becomes narrower, and the HPBW decrease.

Fig. 4. Adapted pattern of linear array for desired signal and three interfering signals.

Fig. 6. Adapted pattern using Dolph-Chebyshev distribution with (SLR=30).

JOURNAL OF TELECOMMUNICATIONS, VOLUME 8, ISSUE 2, MAY 2011 45

6.3 Linear Array with Binomial Distribution Binomial distribution is a Dolph-Chebyschev array with no side lobes. This distribution gives the widest beam width, therefore it gives the best null and the best beam pattern for the systems requiring wide coverage area. This is demonstrated in Fig.7 with (N=6, 9, 12, 15). The direction of the desired signal is at ,  90* . The angular range of interest is I 0* , 180* L. It is shown that the resultant array factor has a peak at the desired direction of ,  90* with

Fig. 8. Adapted pattern of planar array for two desired users and interference user.

Fig. 5. Adapted pattern using Dolph-Chebyshev distribution with (SLR=20).

Fig. 7. Adapted pattern of linear array with Binomial distribution.

no side lobe. As it is expected from the antenna array theory, increasing the number of antenna elements, the beam width of the antenna array becomes narrower, and the half power bean width decrease.

6.4 Planar Array The second implementation consists of [8×8] elements planar array. Planar array is chosen it has more versatile and can provide more symmetrical patterns with lower side lobes. In addition, they can be used to scan the main beam of the antenna toward any point in space. In practice, 2-D arrays are used to enable the system to track a larger number of users. Hence, for illustration purposes our method was applied to 2-D array of isotropic elements. fig8 illustrated that the planar array is used to track two desired signals at ,  80* , 135* , o  45* while simultaneously make nulling at the directions of the interference signal at ,  130* and o  0* . Fig.9 illustrates that the planar array is used to track two desired signals at ,  60* , 110* and o  45* while simultaneously make nulling at the

Fig. 9. Adapted pattern of planar array for two desired users and three interference users.

directions of the three interference signals at ,  0* , 90* , 180* and o  30* .

7 CONCLUSIONS In this paper, a Neural Network algorithm for beamforming problem is presented. Two array applications are implemented to show the performance of the NN beamformer. The weights were computed

© 2011 JOT http://sites.google.com/site/journaloftelecommunications/

JOURNAL OF TELECOMMUNICATIONS, VOLUME 8, ISSUE 2, MAY 2011

using an RBFNN that approximates the Wiener solution. The network successfully, tracked the desired signals while simultaneously placed nulls in the direction of the interfering users. Both 1-D and 2-D arrays were simulated and the results have been found very well in every case. So, it is concluded that the adaptive beamforming algorithm based on neural network consistently enjoys a significantly improved performance as compared with other existing algorithms.

REFERENCES [1]

[2] [3]

[4] [5]

A. H. El Zooghby, C. G. Christodoulou, and M. Georgiopoulos, “A Neural Network-Based Smart Antenna for Multiple Source Tracking,” IEEE Transactions on Antennas and Propagation, AP48,5, May 2000, pp. 768-776. Y. H. Hu, J. N. Hwang, Handbook of neural network signal processing, CRC Press LLC, 2002. M Sarevska, A. M. Salem, N Mastorakis, "Null Steering Beamformer Based on RBF Neural Networks", 12th WSEAS International Conference on COMMUNICATIONS, Heraklion, Greece, July 23-25, 2008. El Zooghby, Ahmed, Smart Antenna Engineering, © 2005 ARTECH HOUSE, INC. S. Caylar, K. Leblebicioğlu, and G. Dural, “A new neural network approach to the target tracking problem with smart structures”, Proc. of IEEE AP-S International Symposium and USNC/URCI meeting, pp. 1121 – 1124, July 2006.

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