IJECT Vol. 10, IssuE 2, AprIl - JunE 2019

w w w . i j c s t . c o m InternatIonal Journal of electronIcs & communIcatIon technology 9

Issn : 0976-8491 (online) | Issn : 2229-4333 (Print)

Adaptive Beamforming Algorithms for Mimo Radar and Phased Array Antennas for Future Generation

Wireless Communications1L. Surendra, 2B.Uday Krishna Kanth, 3Ch.Ruth Suhasini, 4T.Yamini, 5G.Durga Bhavani

1,2,3,4,5Dept. of ECE, Usha Rama College of Engineering, and Technology, Telaprolu, Vijayawada, India

AbstractNext generation wireless communications like 5G and beyond 5G networks for high-speed data transfer rates required with reduced side-lobe levels are indeed required to meet the demands of future wireless communications Many wireless communication and radar signals are frequency or phase modulated signals Some examples of phase and frequency modulated signals are Frequency Modulation (FM), Phase Shift Keying (PSK), Frequency Shift Keying (FSK), Quadrature Amplitude Modulation (QAM) Advanced and sophisticated communication system requires an SLL which is significantly lesser than the main lobe peak This can be achieved by considering CMA and LS-CMA based on CMA These are efficient blind adaptive beamforming algorithms are implemented by using MATLAB.

KeywordsBlind Beamforming, CMA, LSCMA, MIMO Radar, Phased Array Antennas

I. Introduction MIMO radar is an advanced type of phased array radar employing digital receivers and waveform generators distributed across the aperture MIMO radar signals propagate in a fashion similar to Multistatic radar However, instead of distributing the radar elements throughout the surveillance area, antennas are closely located to obtain better spatial resolution, Doppler resolution, and dynamic range. MIMO radar may also be used to obtain Low-probability-of-intercept radar properties A phased array usually means an electronically scanned array, a computer-controlled array of antennas which creates a beam of radio waves that can be electronically steered to point in different directions without moving the antennas. Smart antennas are antenna arrays with smart signal processing algorithms used to identify spatial signal signatures such as the Direction of Arrival (DOA) of the signal, and use them to calculate beamforming vectors which are used to track and locate the antenna beam on the mobile/target. Smart antennas should not be confused with reconfigurable antennas, which have similar capabilities but are single element antennas and not antenna arrays.An adaptive beamforming system relies on principles of wave propagation and phase relationships. See Constructive interference, and Beamforming. Using the principles of superimposing waves, a higher or lower amplitude wave is created (e.g. by delaying and weighting the signal received). The adaptive beamforming system dynamically adapts in order to maximize or minimize a desired parameter, such as Signal-to-interference-plus-noise ratio. Adaptive beamforming techniques includes non-blind and blind beamforming algorithms. In non-blind, we are supposed to give a training sequence for the antennas and in blind beamforming algorithms we are not suggested to supply any training sequence. In this we are discussing about the blind beamforming algorithms only.

II. Constant Modulus AlgorithmMany adaptive beamforming algorithms are based on minimizing the error between a reference signal and the array output. The reference signal is typically a training sequence used to train the adaptive array or a desired signal based on an a prior knowledge of nature of the arriving signals.Godard was the first to capitalize on the constant modulus is in order to create a family of blind equalization algorithms to be used in two-dimensional data communication systems. Specifically Godard’s algorithm applies to phase modulating waveform. Godard used a cost function called a dispersion function of order p and q, after minimization. The cost function is given by

( )( ) [( ) ] p q

pJ Rk E y k −= (1)

Where p is positive integer and q is positive integer both are equals to one and the resulting error signal will be

2( ) ( ) | ( ) ( | ( ) | )p ppe k y k y k R y k−= − (2)

The p=1 case reduces the cost function to form

(3)

(4)

Where,2

1[| ( ) |][| ( ) |]

E s kRE s k

= (5)

If we scale the output estimate s(k) to unity we can write the error signal is

( )( ) ( )| ( ) |

y ke k y ky k

= −

(6)

Thus, the weight vector, in the p=1 and p=2 case becomes as respectively

(7)

(8) The p = 1, q = 2 solution is typical because it provides the deepest nulls of the four configurations and provides the best signal to interference noise ratio (SNR).

III. Least Squares Constant Modulus Algorithm (LS- CMA)One severe disadvantage of the Godard CMA algorithm is the slow convergence time. The slow convergence limits the usefulness

IJECT Vol. 10, IssuE 2, AprIl - JunE 2019 Issn : 0976-8491 (online) | Issn : 2229-4333 (Print)

w w w . i j c s t . c o m 10 InternatIonal Journal of electronIcs & communIcatIon technology

of the algorithm in dynamic environments where the signal must be captured quickly. This also limits the usefulness of cma when channel conditions are rapidly changing.The least-squares CMA can be developed as an extension of theme method of nonlinear least-squares (Gauss’s method) to complex-argument cost functions. Gauss’s method exploits the property that at a cost function with form as below shownThe resulting weight update equation is given by

1( ) [ ( ) ( )] ( ) * ( )Hw l X l X l X l r l−+ = (9)Let,� 1( ) ( ) ( )H

xxR l X l X lK

= (10)And

1( ) ( ) * ( )xxr l X l r lK

= (11)

Then the updated weight vector can be related in terms of time averages for an error signal covariance matrix, � ( )xxR l and estimated cross correlation vector ( )xxr l given by

� 1( 1) ( ) ( )xx xxw l R l r l−+ = (12)

It is apparent to note that again the null formed by the SMI method is deeper than that formed by the LS-CMA algorithm. Additionally, the beam pattern for the LS-CMA algorithm is nearly symmetric. This is due to the fact that when computing the estimate of the covariance matrix, much of the phase information needed is eliminated when multiplying the two input data matrices.The required maxima and minima are produced, but false maxima and minima are also formed at both 45o and -30o due to this phase ambiguity. Also, we can see that the main lobe directed to the desired user does not peak at exactly -45o.

Fig. 1: Phase of Desired Signal and CMA Output

IV. Simulations Results of CMAThis is graph obtained between magnitude of desired signal and CMA output. In the fig. we see that the CMA output is a blue line while the desired output shows a little about the Lms output line.

Fig. 2: Magnitude of Desired Signal and CMA Output

Fig. 3: Error Between Desired Signal and CMA Output

V. Simulations Results of LSCMA

Fig. 4: Amplitudes of Arriving Signal and Output Signal in LSCMA

IJECT Vol. 10, IssuE 2, AprIl - JunE 2019

w w w . i j c s t . c o m InternatIonal Journal of electronIcs & communIcatIon technology 11

Issn : 0976-8491 (online) | Issn : 2229-4333 (Print)

Fig. 5: Plot Between Angle of Arrival Versus Array Factor for Least Square Constant Modulus Algorithm.

V. Conclusion Hence, when the algorithms Constant Modulus Algorithm and Least Squares constant modulus algorithms are compared, we got to know that the side lobe levels of each algorithm are as shown in Table 1.

Table 1:Algorithms Side lobe levels(dB)

Constant modulus 0.2533Least square constant

modulus 0.2708

Therefore, on comparing these algorithms with other algorithms like SMI, RLS, NRLS, LMS obtained higher side lobe reduction results using the blind beamforming algorithms.

VI. AcknowledgmentWe thank the management of Usha Rama college of engineering and technology for all the support and encouragement rendered in this project. We also extended our sincere thanks to the Director K. Rajashekara Rao and Principal G. KVSV Prasad and for providing the required facilities to carry out this work

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SMI and RLS Adaptive Beamforming Algorithms for Smart Antennas”, InternatIonal Journal of Computer Science and Technology, ISSN : 0976-8491(online) ISSN : 2229-4333(print), 2012.

[2] Agee, B.G.,“The Least Squares CMA: A New Technique for Rapid Correction of Constant Modulus Signals”, Proceedings of the IEEE ICASSP. pp. 19.2.1-19.2.4.

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Surendra loya received his B.Tech from JNTUH and M.Tech from Gitam UniversityVisakhapatnam.He is having 10 years teaching experience in various engineering colleges and universities in the department of ECE.Presently he is working in ECE department Usha Rama college of Engineering and Technology.

Uday Krishna Kanth Badida received his degree on bachelor of technology in ECE in Usha Rama college of engineering and technology, Telaprolu, Andhra Pradesh affiliated to JNTUK Kakinada.

IJECT Vol. 10, IssuE 2, AprIl - JunE 2019 Issn : 0976-8491 (online) | Issn : 2229-4333 (Print)

w w w . i j c s t . c o m 12 InternatIonal Journal of electronIcs & communIcatIon technology

Durga Bhavani Gudikandula received her degree on bachelor of technology in ECE in Usha Rama college of engineering and technology, Telaprolu, Andhra Pradesh affiliated to JNTUK Kakinada.

Ruth Suhasini Chabathula received her degree on bachelor of technology in ECE in Usha Rama college of engineering and technology, Telaprolu, Andhra Pradesh affiliated to JNTUK Kakinada.

Yamini Terli received her degree on bachelor of technology in ECE in Usha Rama college of engineering and technology, Telaprolu, Andhra Pradesh affiliated to JNTUK Kakinada.