Fast and Robust Variable-Step-Size LMS Algorithm for Adaptive Beamforming

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. Altho...

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Veröffentlicht in:IEEE antennas and wireless propagation letters Jg. 19; H. 7; S. 1206 - 1210
Hauptverfasser: Jalal, Babur, Yang, Xiaopeng, Liu, Quanhua, Long, Teng, Sarkar, Tapan K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive algorithms
Adaptive beamforming
Algorithms
Array signal processing
Beamforming
Complexity
Computer simulation
Convergence
Interference
least mean square (LMS)
Mathematical analysis
Mean square error methods
Parameters
Performance enhancement
Robustness
sigmoid function
Signal to noise ratio
Steady-state
variable step size (VSS)
ISSN:1536-1225, 1548-5757
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Zusammenfassung: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.
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ISSN:1536-1225
1548-5757
DOI:10.1109/LAWP.2020.2995244