Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 4, Number 2 (2014), pp. 201-206 © Research India Publications http://www.ripublication.com/aeee.htm

Performance Analysis of Smart Antenna Using Beam Forming Techniques

Pradeep Kumar Nayak1, Sachidananda Padhi2 and Subrat Sethi3

1Research Scholar (B.U), Berhampur, Ganjam,Odisha. 2Sachidananda Padhi, M.Tech (I.I.T, KGP), W.B.

3Subrat Sethi, M.Tech (I.I.T, KGP), W.B. E-mail: 1pradeep190476nayak@gmail.com, 2sachi579padhi@gmail.com,

3subrat.iitkgp@hotmail.com

Abstract Next generation wireless communication epoch establish significant developmental platform on characterizing and designing of Smart antenna, as it enhances the performance of mobile communication systems, by increasing channel capacity, spectrum efficiency, coverage range and steering multiple beams to track multiple mobiles. The most important feature of a smart antenna system is its beam forming capability. Smart antennas usually include both switched beam forming and adaptive beam forming systems. Several fixed beam patterns are available in Switched beam systems i.e. a decision is made as to which beam to access, at a particular time. Whereas Beam formed adaptive systems allow the antenna to steer the beam to any direction of interest while simultaneously nullifying interfering signals. In this paper, comparative analysis of BER vs Eb/N0, with and without beam forming, in a multipath Rayleigh fading channel is carried out by MATLAB simulation. Keywords: Adaptive beam forming, smart antenna system, switched beam forming.

1. Introduction A single antenna element is mostly omni–directional. If a base station uses an omni-directional antenna and a user communicates with this station, every signal that is send back and forth between the two devices is at the same time a source of interference for any other communication taking place within the same cell.

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Smart antenna systems are rapidly emerging as one of the key technologies that can enhance overall wireless communications system performance, by making use of the spatial dimension, and dynamically generating adaptive receiving and transmitting signal beams. Beamforming or spatial filtering is a signal processing technique, by which the smart antenna creates a directional beam toward the desired user and nulls the signal in the directions of undesired users by appropriately adjusting the magnitude and phase of the signal transmitted by each of its elements.

Fig. 1: Block diagram of smart antenna.

Depending on the signal processing methods and the adaptive schemes used, smart

antenna techniques can be separated into three broad categories: (a) Diversity, (b) Spatial Multiplexing (SM), and (c) Beamforming. Roughly speaking, beamforming aims at improving Signal to Interference plus Noise Ratio (SINR), diversity aims at reducing the variations in the SINR experienced by the receiver, while SM aims at sharing SINR in high SINR scenarios.

In comparison to Omni-directional transmissions, beam forming reduces interference, allowing more concurrent transmissions in the network. Moreover, by concentrating the transmission energy in a specific direction, beamforming creates a signal that is in order of the magnitude stronger than that of the signals in other directions. This technique can be used to increase the coverage of a particular area or data rate or the spectral efficiency of the system.

There are two basic types of smart antennas. As shown in Fig 2, the first type is the phased array or multi beam antenna, which consists of either a number of fixed beams with one beam turned on towards the desired signal or a single beam (formed by phase adjustment only) that is steered towards the desired signal.

The other type is the adaptive antenna array as shown in Fig 3, which is an array of multiple antenna elements, with the received signals weighted and combined to maximize the desired signal to interference plus noise power ratio. Prior to adaptive beamforming, the directions of users and interferes must be obtained using a direction-of- arrival (DOA) estimation algorithm. The goal of DOA estimation is to use the data received on the downlink at the base-station sensor array to estimate the directions of

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the signals from the desired mobile users as well as the directions of interference signals. The results of DOA estimation are then used to adjust the weights of the adaptive beam former so that the radiated power is maximized towards the desired users, and radiation nulls are placed in the directions of interference signals. Hence, a successful design of an adaptive array depends highly on the choice of the DOA estimation algorithm which should be highly accurate and robust.

Fig. 2: Phased Array Fig. 3: Adaptive Array.

This essentially puts a main beam in the direction of the desired signal and nulls in

the direction of the interference antenna patterns. So applying the above principles a smart antenna can greatly reduce interference, increase the system capacity, increase power efficiency as well as reduce overall infrastructure costs. A smart antenna is therefore a phased or adaptive array that adjusts to the environment. That is, for the adaptive array, the beam pattern changes as the desired user and the interference move; and for the phased array the beam is steered i.e. different beams are selected as the desired user moves.

2. System Model It is assumed that the system supports M users (signal sources), and each user transmits a BPSK modulated signal on the same carrier frequency of ω = 2πf. The baseband signal of user i is formulated as:

푚 (푘) = 퐴 퐵 (푘), 1 ≤ 푖 ≤ 푀, (1) where bi(k) є {±1} and 퐴 denotes the signal power of user i.. The linear antenna

array considered consists of L uniformly spaced elements, and the signals received by the L-element antenna array are given by:

푥 (푘) = ∑ 푚 (푘) exp 푗휔푡 (휃 ) + 푛 (푘) = 푥 + 푛 (푘), 1 ≤푙 ≤ 퐿, (2)

where 푡 (휃 ) is the relative time delay at array element l for source i, 휃 is the direction of arrival for source i, and nl(k) is the complex-valued white Gaussian noise having a zero mean and a variance of E[|nl(k)|2] = 2휎 . The desired user’s signal to noise ratio is defined as SNR=퐴 /2휎 , the interference to noise ratio of user i is given by 푆푁푅 = 퐴 /2휎 , and the desired signal to interference ratio with respect to user i is

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defined as 푆푁푅 = 퐴 /퐴 , for i = 2, ・ ・ ・, M. In vectorial form, the array input x(k) = [x1(k) x2(k) ・ ・ ・ xL(k)]T can be expressed as:

푥(푘) = 푥̅(푘) + 푛(푘) = 푃푏(푘) + 푛(푘) (3) where n(k) = [n1(k) n2(k) ・ ・ ・ nL(k)]T has a covariance matrix of E[n(k)nH(k)]

=2휎 퐼 with IL representing the L × L identity matrix, the system matrix P is given by:

푃 = [퐴 푠 퐴 푠 … … 퐴 푠 ], (4) the steering vector for source i is formulated as:

푠 = [푒 ( ) 푒 ( ) . . . e ( ) ] (5) and the transmitted bit vector is b(k) = [b1(k) b2(k) ・ ・ ・ bM(k)]T . The beamformer’s output is given by:

푦(푘) = 푤 푥(푘) = 푤 푥̅(푘) + 푤 푛(푘) = 푦(푘) + 푒(푘), (6) where w = [w1 w2 wL]T is the complex-valued beamformer weight vector, and e(k)

is Gaussian distributed having a zero mean and a variance of E[|e(k)|2] =2휎 푤 푤 . The estimate of the transmitted bit b1(k) is given by:

푏 = +1, 푦 (푘) > 0, −1, 푦 (푘) ≤ 0, (7)

Where yR(k) = R[y(k)] denotes the real part of y(k) Classically, the beamformer’s weight vector is determined by minimizing the MSE term of E [|b1(k) − y(k)|2] between the desired user’s transmitted bit and the beamformer’s output

The p.d.f. of yR(k) can be shown to be explicitly given by: 푝(푦 ) =

√∑ exp − ( , ) (8)

and the BER can alternatively be expressed as: 푃 (푤) = ∑ 푄(푔 (푤)) (9)

where 푔 (푤) = ( , ) , and y , є yR.

3. Simulation Results Here in this section simulation results are carried out using MATLAB. Fig 4 shows the beam radiation pattern having four array elements, where the desired signal angle of incidence is 450 . Fig 5 shows the BER vs. Eb/N0 with and without using beam forming technique for a 2 tap channel. The BER vs. Eb/N0 can also be obtained for N-tap channel. The BER curve is better with beamforming technique compared to without beam forming technique. Fig 6 shows the BER vs. Eb/N0, when 2 phase array antennas are used (beamforming) and without beamforming .It shows better result when beam forming technique is implemented.

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Fig. 4: Beam radiation pattern.

Fig. 5: BER of BPSK in Rayleigh channel 2 tap channel.

Fig. 6: BER of BPSK in Rayleigh channel with 2 phase array antenna.

4. Conclusion This paper highlights the beamforming radiation pattern and also the comparison study of BER vs. Eb/N0, with and without beamforming in Rayleigh channel. The simulation results demonstrate a significant performance improvement using beamforming techniques with the presence of large interferences. The performance also improves with the increasing number of the array antennas at the transmitter and receiver.

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