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Fast and Robust Variable-Step-Size LMS Algorithm for Adaptive Beamforming
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DOI：10.1109/LAWP.2020.2995244.png)
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Abstract
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Conventional least-mean-square (LMS) algorithm is one of the most popular algorithms, which is widely used for adaptive beamforming. But the performance of the LMS algorithm degrades significantly because the constant step size is not suitable for varying signal-to-noise ratio (SNR) scenarios. Although numerous variable-step-size LMS (VSS-LMS) algorithms were proposed to improve the performance of the LMS algorithm; however, most of these VSS-LMS algorithms are either computationally complex or not reliable in practical scenarios since they depend on many parameters that are not easy to tune manually. In this letter, a fast and robust VSS-LMS algorithm is proposed for adaptive beamforming. The VSS is obtained based on normalized sigmoid function, where the sigmoid function is calculated by using the mean of instantaneous error first and then normalized by the squared cumulative sum of instantaneous error and estimated signal power. The proposed algorithm can update the step size adaptively without tuning any parameter and outperform state-of-the-art algorithms with low computational complexity. The simulation results show better performance of the proposed algorithm.
Keywords:
Signal to noise ratio
Mean square error methods
Interference
Array signal processing
Convergence
Steady-state
Robustness
Adaptive beamforming
least mean square (LMS)
sigmoid function
variable step size (VSS)
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