A General Constrained Adaptive Filtering Algorithm for SISO Channel Estimation and Beamforming

In wireless communications and vehicle communications, it is useful to use adaptive filtering techniques for channel estimation, beamforming and echo cancellation. In this paper, we propose a general constrained adaptive filtering (GCAF) algorithm for single channel estimation and beamforming, which is obtained by integrating a general and adaptive loss function into the constrained adaptive filtering (CAF) framework. By selecting the parameter in the GCAF, it can approximate to several popular CAF algorithms. Then, the convergence, stability boundary and the stability analysis of the mean squared-deviation have been analyzed and presented in detail. Additionally, The complexity of the GCAF is presented and compared with the existing algorithms. The proposed GCAF is used for single-input and single-output (SISO) channel estimation and beamforming under different noises, and the tracking performance of the GCAF is also analyzed. The simulation results demonstrate that the GCAF algorithm outperforms the typically adaptive filtering algorithms and can effectively approxiamte the similar algorithms under heavy-tailed noises, which makes the proposed GCAF more robust and general.