A New Variable-Regularized Transform-Domain NLMS Algorithm with Automatic Step-Size Selection for Adaptive System Identification/ Filtering

Chan, S. C.; Chu, Y. J.

doi:10.1007/s11265-015-1036-y

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Also, the steady-state misadjustment and convergence speed of LMSlike algorithms are improved by introducing variable step size (VSS) [11]. Fractional evolutionary strategies and fractional derivative-free algorithms formulated using fractional LMS and NLMS [12][13][14][15] have been proposed to improve accuracy and convergence. A complex quaternion LMS algorithm is implemented [16] for beam-forming of polarization-sensitive electromagnetic vector-sensor.…”

Section: Introductionmentioning

confidence: 99%

Performance of Smart Antenna Under Different Fading Conditions

Senapati

Patro

Sooksood

et al. 2021

Wireless Pers Commun

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Optimizing the current distribution of an evenly spaced antenna array has shown to be an efficient approach for reducing side lobe levels. In this article, the Tchebyscheff distribution-based antenna array synthesis approach is combined with an adaptive signal processing algorithm for beamforming and side lobe level reduction in smart antennas in various fading situations. The performance of smart antennas in uniformly spaced linear, planar, circular, and semi-circular arrays are evaluated. The presence of Rayleigh and Rician channels is examined in the network. The least mean square (LMS) and normalised least mean square (NLMS) algorithms are applied as adaptive algorithms. In fading environments, the NLMS algorithm with Tchebyscheff distribution outperforms than the LMS algorithm with Tchebyscheff distribution, with a side lobe level decrease of 11.23 dB. The lowest side lobe achieved with the NLMS algorithm with Tchebyscheff distribution is − 45.59 dB for uniform planar array.

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