Beamforming of Smart Antenna Using a Combined Tchebyscheff Distribution and Constant Modulus Algorithm Subhashree Nibedita Baliarsingh, Anupama Senapati, Arindam Deb, Jibendu Sekhar Roy School of Electronics Engineering, KIIT University, Bhubaneswar 751024, Odisha, India subhashreeb4@gmail.com, senapati.anupama@gmail.com, adebfet@kiit.ac.in, drjsroy@rediffmail.com Abstract—The objective of this paper is to improve the performance of adaptive smart antenna using a hybrid algorithm combining constant modulus algorithm (CMA) and array synthesis method. Adaptive beamforming using Tchebyscheff distribution, an array synthesis method, along with constant modulus algorithm (TDCMA) is compared with the performances of beamforming using constant modulus algorithm only. Reduction of side lobe level is an important task to minimize unwanted interference for other users in mobile network. Side lobe level of about 9 dB lower than CMA algorithm is achieved using TDCMA algorithm. Investigations, presented here, may be useful for mobile network. Keyword-Adaptive beamforming, Constant modulus algorithm, Tchebyscheff distribution, Side lobe level reduction I. INTRODUCTION In mobile communication, network performance can be improved by using adaptive smart antenna. Smart antenna is an antenna array with digital signal processing unit which after estimating direction of arrival (DOA), generates radiation beam along the desired user and produces null towards the undesired interferer [1-3]. Appreciable power saving is possible using smart antenna in mobile network in addition to enhancement of signal quality, network capacity and coverage area [4]. In smart antenna, the received signals at the different antennas are multiplied with complex weights and then adaptively weights are summed up [4-5]. Various types of beamforming algorithms, having their advantages and disadvantages are available [3-8]. In addition to various methods of DOA estimations, many iterative schemes applicable to adaptive beamforming have been described [3]. A sequential quadratic programming based algorithm is used for multi-lobe pattern and for adaptive nulling of the pattern [5]. A complex quaternion least mean square (LMS) algorithm is used [6] for beamforming of polarization-sensitive electromagnetic vector- sensor. An adaptive algorithm to control main beam and side lobe of adaptive array, based on amplitude approaching algorithm, is reported [7]. Numerical and experimental results for an eight elements linear smart antenna array using a set of electronically driven vector modulators, are described in [8]. To improve the performance of adaptive smart antenna, array synthesis methods are used in [9-10]. Genetic algorithm is coupled with the Schelkunoff synthesis method for uniform linear and planar array design [9]. Tchebyscheff distribution (TD) is used with beamforming constraint stability least mean square (CSLMS) algorithm for adaptive antenna [10]. In this paper, array synthesis method, Tchebyscheff distribution (TD) for current amplitude distribution in linear antenna array of 16 antenna elements is used with constant modulus algorithm (CMA) [11-12]. First, CMA algorithm is used for beam formation for adaptive smart antenna and then the program is modified to incorporate Tchebyscheff distribution along with CMA algorithm (TDCMA). Side lobes are causes of interferences for other users in a mobile cell. Attention is given to reduce side lobe level using the new proposed algorithm. II. ADAPTIVE BEAMFORMING OF SMART ANTENNA USING CMA AND TDCMA Constant modulus algorithm (CMA) exploits the constant modularity of the signal to adapt the parameters [11-12].The algorithm is best used with signals that have a constant envelope. If x(k) is the input signal, then the output signal y(k) is given by [11-12] ) ( ) ( ) ( k x k w k y H CMA  (1) The weight (w) updating equation is similar to LMS algorithm, but the error is computed from the actual received signal, not from a training sequence. Weight updating equation is given by [11-12] ) ( ) 1 ) ( )( ( ) ( ) 1 ( 2 k y k y k x k w k w H CMA CMA      (2) Where H denotes the transpose conjugate and μ is the step size parameter. ISSN (Print) : 2319-8613 ISSN (Online) : 0975-4024 Subhashree Nibedita Baliarsingh et al. / International Journal of Engineering and Technology (IJET) DOI: 10.21817/ijet/2017/v9i4/170904167 Vol 9 No 4 Aug-Sep 2017 3248