Adaptive beamforming algorithms for smart antenna systems

TLDR

Wireless communication is rapidly expanding, driving demand for higher capacity, which has spurred the development of space‑selective technologies such as smart antennas to address spectrum congestion. The study compares two non‑blind adaptive beamforming algorithms, LMS and NLMS, to determine which yields better performance in a robust smart antenna system. The authors implemented LMS and NLMS adaptive beamforming on smart‑antenna arrays and evaluated them via MATLAB simulations. NLMS outperforms LMS in multiple metrics, leading the authors to recommend NLMS for mobile companies deploying smart antennas.

Abstract

Wireless communication is one of the most rapidly growing industries. The high demand for wireless communication services had led to an increase in system capacity. Then most elementary solution would be to increase bandwidth; however, this becomes ever more challenging as the electromagnetic spectrum is becoming increasingly congested. The ever-increasing demand for increased capacity in wireless communications services has led to developments of new technologies that exploit space selectivity. This is done through smart-antenna arrays and the associated adaptive beamforming algorithms. Smart-antenna systems provide opportunities for higher system capacity and improved quality of service among other things In this paper, two non-blind algorithms: Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) algorithms were compared for a robust smart antenna system. It has been found that NLMS performs better in many respects than LMS and so we propose NLMS to be used by mobile companies when they will use smart antenna. Our findings are explained in details in the result and analysis section with graphs. Our comparison and findings were simulated using MATLAB.