Improving LMS/NLMS-Based Beamforming Using Shirvani-Akbari Array

ULA is the most common geometry exploited in array signal processing. In the beamforming operation, employing the ULA leads to obtaining narrower beamwidth with respect to other geometries in similar element numbers. Recently, Shirvani and Akbari proposed a new array by adding two elements to the ULA in top and bottom of the array axis, named as SAA. This new array offers a considerable improvement in DOA estimation performance in detection and resolution of signal sources placed at angles close to the array endfires. In this article, the performance of the proposed SAA is investigated especially in beamforming and compared with ULA. LMS and NLMS algorithms that are popular adaptive beamforming methods are used for evaluation and comparing the performance of SAA and ULA. Considering array factor, mean square error and bit error rate metrics, simulation results show improved convergence speed and higher data transmission accuracy in different signal source locations, boresight angles as well as endfire ones, for SAA with respect to ULA.