Adaptive Array Beamforming Using a Combined LMS-LMS Algorithm

• Published in: IEEE transactions on antennas and propagation Vol. 58; no. 11; pp. 3545 - 3557
• Main Authors: Srar, Jalal Abdulsayed, Kah-Seng Chung, Mansour, Ali
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive array beamforming; Algorithm design and analysis; Algorithms; Antennas; Applied sciences; Array signal processing; Arrays; Beamforming; Computer Science; Computer simulation; Convergence; Eigenvalues and eigenfunctions; Engineering Sciences; Error signals; error vector magnitude (EVM); Exact sciences and technology; least mean square-least mean square (LLMS) and least mean square (LMS) algorithms; Least mean squares; Least mean squares algorithm; Least squares approximation; Radiocommunications; Rayleigh fading; Signal and Image Processing; Signal processing algorithms; Studies; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transmission and modulation (techniques and equipments); Performance evaluation; AWGN; Adaptive algorithm; Input signal; Computer simulation; Adaptive antenna arrays; Beam forming; error vector magnitude (EVM); least mean square-least mean square (LLMS) and least mean square (LMS) algorithms; Antenna factor; Rayleigh fading; Adaptive array beamforming; Least mean squares methods; Signal to noise ratio
• ISSN: 0018-926X; 1558-2221
• DOI: 10.1109/TAP.2010.2071361

A new adaptive algorithm, called least mean square- least mean square (LLMS) algorithm, which employs an array image factor, , sandwiched in between two least mean square (LMS) algorithm sections, is proposed for different applications of array beamforming. It can operate with either prescribed or adaptive . The convergence of LLMS algorithm is analyzed for two different operation modes; namely with external reference or self-referencing. The range of step size values for stable operation has been established. Unlike earlier LMS algorithm based techniques, the proposed algorithm derives its overall error signal by feeding back the error signal from the second LMS algorithm stage to combine with that of the first LMS algorithm section . Computer simulation results show that LLMS algorithm is superior in convergence performance over earlier LMS based algorithms, and is quite insensitive to variations in input signal-to-noise ratio and actual step size values used. Furthermore, LLMS algorithm remains stable even when its reference signal is corrupted by additive white Gaussian noise (AWGN). In addition, the proposed LLMS algorithm is robust when operating in the presence of Rayleigh fading. Finally, the fidelity of the signal at the output of an LLMS algorithm beamformer is demonstrated by means of the resultant values of error vector magnitude (EVM) and scatter plots.