Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results

• Published in: Engineering reports (Hoboken, N.J.) Vol. 3; no. 1
• Main Authors: Imtiaj, SK, Misra, Iti Saha, Bhattacharya, Sandipan
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2021; Wiley
• Subjects: Adaptive algorithms; adaptive beamforming; Algorithms; Antenna arrays; Antenna design; Antennas; Arrays; Beamforming; Cellular communication; comparative study; complexity analysis; Digital signal processing; Digital signal processors; Eigenvalues; Field programmable gate arrays; Iterative methods; LMS; Model testing; multiple interferers; Music; NLMS; Noise; Recursive methods; RLS; Sidelobes; Smart antenna systems; smart antennas; SMI; testbed results; Wireless communications
• ISSN: 2577-8196; 2577-8196
• DOI: 10.1002/eng2.12295

Smart antennas are becoming popular in the area of cellular wireless communication for capacity enhancement and reduction of the multipath effect and interference. The demand for smart antennas is widely increasing as 5G cellular communication evolves to support the higher data rate and bandwidth. The basic principle of smart antenna design is adaptive beamforming using the best suited digital signal processing algorithms, such as least mean square (LMS), normalized least mean square (NLMS), sample matrix inversion (SMI), and recursive least square (RLS), each having its pros and cons. Among these, the LMS and NLMS are iterative approaches while SMI is a block adaptive method and RLS is a recursive method. The contribution of this article includes easy implementation of four adaptive beamforming algorithms, namely LMS, NLMS, SMI, and RLS. Furthermore, an exhaustive comparative performance analysis is carried out under five interferers and evaluated in terms of beamwidth, null depth, maximum sidelobe level, rate of convergence, error variation for the number of antenna elements, and spacing. Finally, a contrast table is presented to demonstrate the pros and cons of the listed algorithms. Time and dynamic space complexity are studied for RLS and SMI beamforming algorithms. Performance results from a designed reconfigurable testbed model for weight adaptation and smart beamforming are also presented for analysis and a better understanding of the real implementation of the smart algorithms through testbed design. The simulation and testbed results create the ground work for future exploration in the design of smart antenna beamforming. Smart antennas are becoming popular in cellular wireless communication. The basic principle of smart antenna design is adaptive beamforming using the best suited digital signal processing algorithms. This paper illustrates four adaptive beamforming algorithms, namely LMS, NLMS, SMI, RLS. An exhaustive comparative performance analysis is carried out under five interferers and evaluated in terms of beamwidth, null depth, maximum sidelobe level, rate of convergence, error variation for the number of antenna elements, and spacing. A contrast table is built to understand the pros and cons of the listed algorithms.