Wireless communication using Adaptive Smart Antenna System

Wireless communication using Adaptive Smart Antenna System

A.N. Jadhav (1) V. M. Mhalgi (2) D.D. Khumane (3)

(1) Department of Electronics & Telecommunication Engineering, D.Y. Patil College of Engineering & Technology, Kasaba Bawada, Kolhapur, Maharashtra, India.

(2) Department of Electronics & Telecommunication Engineering, S.T.B. College of Engineering, Tuljapur, Maharashtra, India.

(3) Department of Electronics & Telecommunication Engineering, S.T.B. College of Engineering, Tuljapur Maharashtra, India

.

Abstract

This paper presents wireless communication and its simulation results using adaptive smart antenna system based on direction of arrival estimation and null steering. Direction of arrival (DOA) estimation is based on the PM algorithm for identifying the directions of the desired signals and the null steering beamformer adaptively adapts the antenna pattern to steer the main beam towards the desired user and nullify all other interference. This system can be used to reduce multipath, co-channel interference and provide value added services . These benefits include the enhancement of coverage and the channel capacity, lower transmitted power, better signal quality, higher data rate and providing value-added services such as users position location (PL) and at the same time to minimize interference arising from other user by introducing nulls in their direction. Also this paper deals with adaptive beam forming approach for the dynamic case based on smart antennas and adaptive algorithms used to compute the complex weights lik e Least Mean Square (LMS) algorithm.

Keywords Sma rt Antenna Technology, Beamforming, Direction-of-Arrival (DOA) Estimation, Multip le Signal classification (M USIC), PM (Propagator Method), Least Mean Square (LMS).

1. Introduction

   There is an ever increasing demand on mobile wireless operators to provide voice and high speed data services. At the same time, these operators want to support more users per base station to reduce overall network cost and make the services affordable to subscribers. As a result, wire less systems that enable higher data rates and higher capabilities are pressing need. Unfortunately because the available broadcast spectrum is limited, attempts to increase traffic within a fixed bandwidth create more interference in the system and degrade the signal quality. When omni-direct ional antennas are used at the base station, the transmission and reception of each users signal becomes a source of interference to other users located in the same cell, ma king the overall system interference limited.

   The demand for wireless services has risen dramat ically fro m few years. Wire less communication systems are evolving from the second generation systems to the third and fourth generation systems, which will provide high data rate multimed ia services as video transmission. New value added services such as the position location (PL) services for e merg ing calls, the fraud detection, intelligent transportation systems, and so fourth are also coming in to reality[1,2,3].

   The smart antenna systems can generally be classified as either switched beam or adaptive array systems. In a switched beam systems can generally be classified as either switched beam or adaptive array systems. In a switched beam system multip le fixed beams in predetermined directions are used to serve the

   users. in this approach the base station switches between several beams that gives the best performance as the mobile user moves through he cell. Adaptive bea m forming uses antenna arrays backed by strong signal process capability to automatica lly change the beam pattern in accordance with the changing signal environment. It not only directs ma ximu m rad iation in the direction of the desired mobile user but also introduces nulls at interfering directions while trac king the desired mobile user at the same time. The adaptation achieved by mult iplying the incoming signal with comple x we ights and then summing them together to obtain the desired radiation pattern. These weights are computed adaptively to adapt to the changes in the signal environment. The comp le x weight computation based on diffe rent criteria and incorporated in the signal processor in the form of software algorith ms like Least Mean Square. [6]

   A smart antenna technology can achieve a number benefits like increase the system capacity, greatly reduce interference, increase power effic iency [4, 5]. In the following section we revie w on the smart antenna technology with the help of simulation by using MATLAB.

2. Basics of DOA Estimation & Beamforming

   Since most RF antennas amplifie rs, mixers, filters and ADC technologies have reached a mature state, accurate estimation of the angle of a rrival of signals impinging an array of antennas becomes the most important para meter regard ing the performance of an adaptive array. Assuming a linear and isotropic transmission mediu m, mu ltip le imp inging wave fronts can be modeled as the superposition of these wave fronts imping ing on the array. It is therefore necessary for the DOA estimation algorithm to be able to resolve imping ing and often fully coherent wave fronts into their respective DOAs. Many DOA estimation algorithms e xist, but only a few have found use in smart antennas

   i.e. conventional methods, linear predict ion methods, eigenstructure methods and estimat ion of signal parameters via Rotational invariance techniques (ESPRIT) [6]. All these methods are based on the digital beamforming (DBF) antenna array. Signals received by individual antenna ele ments, are down converted to base band signal then they are digitized and fed into a digita l

   signal processing (DSP) chip where the DOA estimation algorith m is e xecuted. In th is paper we ta ke a b rie f review on the DOA estimation using PM algorith m for finding the PL and LM S algorithm uses the estimate of the gradient vector fro m the available data. LM S algorith m is important because of its simplic ity and ease of the computation.

   The delay and sum bea mformer is attractive because of its simp lic ity and ease of imp le mentation. The limit ing factor method is that though it can steer its main beam it has no control over its side lobes. The solution to this problem is the null steering beamformer a lso minimize the signal to interference ratio and for direction of arrival estimation (DOA ) and beamforming wou ld like to simulate Estimator and bea mformer as follows fig.1.

   Radi o

   Unit

   DOA

   Estim at ion

   Element w

   1

   w

   Element 2

   w

   Element M

   DSP Processor ( DOA &

   Beamform ing Algorithm )

   Fig. 1 Block Diagram of Smart antenna Propagator method for DOA estimation and beamforming

3. Simulation Study And Results

   The simulat ion is developed in MATLAB the following parameters are used for the DOA estimation. First set the noise properties SNR=10 antenna properties, M=5 nu mber of ele ments in antenna array, N=100 number of times steps, dt and t are the length of time step and time vector. Then set the incoming signal properties, L=1 nu mber of inco ming signals, f0 = 1×109 inco ming signal frequency, set amplitude and phase then round up

   the data noise. Calculate the matrix that content the antenna outputs

   350

   the respective NAF amplitude is shown here the user at 650, 350, 1250 & 1500 is the interferer

   Desire signal

   300

   250

   Power Spectrum

   200

   150

   120

   90

   1

   0.8

   0.6

   0.4

   Interfe rence signal

   60

   30

   150

   100

   50

   0

   -50

   0 20 40 60 80 100 120 140 160 180

   Desire DOA (azimuth) angle

   Fig. 2 PM Histogram for DOA (azimuth)estimation using parallel array configuration

   X (t ) =A()*s(t) + n(t) (1)

   Where A=steering vector, s=signal received at first antenna. Then initialization of covariance matrix Rxx, then the PM spectrum are shown in fig. 2, these spectrums are rando mly taken.

   And for the beamformers simulation, set f0, and d=0.5 d istance between antenna element, M=5 nu mber of array ele ments, L=1 total number of users, set the additional variab le like c- speed of light la mbda- wavelength, k-wave number, then generate the norma lized a rray factor and plot the radiation pattern, and user position, in fig.4 where a side lobe allows the interfering signal a lthough attenuated to reach the receiver a fter the we ights are applied and the solution is null steering. Fig. 3, here the desired user at the 950 and

   0.2

   180 0

   Fig. 3. Null steering beamformer

4. CONCLUSION

   Here the simu lation for the DOA estimation, compute a spatial spectrum then estimate DOAs. These methods apply weights to each element in the array so as to steer the antenna pattern towards a known look direction. Once a DOA is estimated, the beamforme r adapts the antenna pattern to steer the main bea m towards the desired user and place nulls in the unwanted direction.

   It has advantage of analyzing the signals of arriving antenna array, using beams fle xibly and optimistically reducing the probability of interfering and being interfered, enhancing frequency utilization efficiency, and imp roving system performance.

5. References

   1. T.S. Rappaport, J.H. Reed and B.D. Woerner, Position location using Wireless Communications on highways of the future IEEE communications Magazine, vol. 34, no.10, pp. 33-41, 1996.

   2. G.V. Tsoulos, Smart antennas for mobile communication systems: benefits and challenges, IEEE Electronic & Communication Engineering Journal, Vol 11, no.2 pp. 84-94, 1999.

   3. 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.

   4. Tsoulos G.V. M each M A. Swalas S.C. Adaptive Antennas for Third Generation DS- CDM A Cellular Systems, Proceedings of the 45th Vehicular Technology Conference, Vol1, pp 45-49, July 1995.

   5. Okamoto G.T. Smart Antennas and Wireless LANS. Kluwer Academic Publishers, Norwell Mass., 2002

   6. Godara, L.C., Application of Antenna Arrays to M obile Communications, Part II: Beamforming and Direction-of Arrival Considerations, Proceedings of the IEEE, Vol. 85, No. 8 pp 1195-1245, August 1997.