Beamforming is one of the most significant processes in smart antennas. To change the antenna beam pattern is the major function of beamforming for a given angle. The algorithm that is adaptive beamforming is utilized to choose the conventional weights of each array element from acquired data of array antenna to extract the desired source signal while cancelling noise and interference. A lot of algorithms are already existing for antenna beamforming technique but they all experience low convergence. So this paper deals with an iRLS algorithm for antenna beamforming technique with Particle Swarm Optimized (PSO) FFT filter. As a result, antenna beamforming based on iRLS shows fast convergence with reduced design complexity. The overall work is simulated in MATLAB. The parameters like amplitude, bit error rate, capacity, SINR and error function values are evaluated. Our work is compared with existing Applebaum, Recursive Least Squares (RLS) (with/without filter) and Least Mean Square (LMS) algorithm. Fast convergence is occurred by iRLS when compared with existing algorithms. The iRLS is converged at 90th iteration, whereas existing algorithms likewise RLS with pre-filter, RLS without pre-filter, LMS and Applebaum is converged at 200, 400, 600 and 850th iteration. So here, our proposed iRLS gives better performance when compared with others.

Design of iRLS Algorithm With/Without Pre-Filter for Antenna Beam Forming Technique

Smart antenna improves the gain of the main lobe in a direction of arrival and null generation toward the interference. Using this technique, the direction of arrival (DOA) of the antenna array can be improved and array factor can be derived in the desired direction of Angle of arrival. A report on performance evaluation of adaptive beam steering generation using Least Mean Square (LMS) and Recursive Least Mean Square (RLS) algorithms is presented. LMS algorithm is simple in the computation of Beam Forming. By repeated corrections of the weights, in an iterative procedure, the LMS algorithm finds the best weights. RLS algorithm exhibits very fast conjunction though at the cost of high complexity of computation. The effectiveness of these optimization algorithms would be compared with respect to run time. The two algorithms are compared with respect to less run time while maintaining the required specifications of the antenna is discussed. The simulation of all the results would be carried using MATLAB.

Adaptive Beam Steering of Smart Linear Array Using LMS and RLS Algorithms

Etching multi-resonant shapes on the radiating patch of a microstrip antenna is often used to interpret multiband characteristics. In this paper, such a multiband antenna is designed using aperture-coupled feed. The proposed multi-resonant geometry captivates the advantages of both planar microstrip patch antenna (MPA) and the aperture feed system. The antenna is simulated in high-frequency structure simulation (HFSS) tool and analyzed using radiation characteristics plots like reflection coefficient voltage standing wave ratio (VSWR) and polar radiation plots.

B. Roopa

Papers

Adaptive Beam Steering of Smart Linear Array Using LMS and RLS Algorithms

Multiband Characteristics of the Slot-Loaded Aperture Fed Antenna