## 4 Results and Discussion
### Scenario 2: Sources with Mixed Stable Distribution
Furthermore, the rest of the FSS-based algorithms require tuning the step parameters as well as exhibit a trade-off situation for the fast convergence and good steady-state performance. It is noticed that the proposed CCSS-MFxRRLS algorithm clearly demonstrates the best performance among the algorithms considered in this paper. Figure 11: Typical alpha stable signals generated from mixed distribution (49): (**a**) 50% samples from \(\alpha=2.0\) (Gaussian) and 50% samples from \(\alpha-\)stable distribution with \(\alpha=1.1\) (variance of shown signal is found to be \(5.4953\times 10^{3}\)); (**b**) 75% samples from \(\alpha=2.0\) (Gaussian) and 25% samples from \(\alpha-\)stable distribution with \(\alpha=1.1\) (variance of shown signal is found to be \(0.5527\times 10^{3}\)); (**c**) 75% samples from \(\alpha=2.0\) (Gaussian) and 25% samples from \(\alpha-\)stable distribution with \(\alpha=1.6\) (variance of shown signal is found to be \(0.0042\times 10^{3}\)). Figure 12: Detailed simulation results for reference signals from mixed distribution (49) with \(\tau=0.5\), \(\alpha_{1}=2.0\), and \(\alpha_{2}=1.1\): (**a**) [PERSON]’s algorithm [24]; (**b**) Th-FxLMS algorithm [25]; (**c**) modified FxLMK algorithm; (**d**) INSS-FxLMS algorithm [26]; (**e**) effect of step-size parameter in the proposed FxRLS-based impulsive ANC algorithms; (**f**) performance comparison between various algorithms: (1) [PERSON]’s algorithm (\(\mu=1\times 10^{-8}\)); (2) Th-FxLMS algorithm (\(\mu=5\times 10^{-6}\)); (3) modified FxLMK algorithm (\(\mu=1\times 10^{-4}\)); (4) INSS-FxLMS algorithm (\(\mu=1\times 10^{-4}\)); (5) previous CCSS-NMFxLMS algorithm (\(\mu_{1}=1.0,\mu_{2}=1\times 10^{-2}\)); (6) FSS-MFxRLS algorithm (\(\mu=0.1\)); (7) proposed CCSS-MFxRRLS (\(\mu_{1}=1.0,\mu_{2}=0.05\)). The simulation results for noise sources from a mixed distribution (49) with \(\tau=0.75\), \(\alpha_{1}=2.0\), and \(\alpha_{2}=1.6\) are presented in Figure 14. As described earlier, the reference signal is mostly (75%) Gaussian, with (25%) impulsive samples coming from a less impulsive distribution as compared with the previous experiments. Therefore, all algorithms considered  
Figure 13: Detailed simulation results for reference signals from mixed distribution (49) with \(\tau=0.75\), \(\alpha_{1}=2.0\), and \(\alpha_{2}=1.1\): (**a**) [PERSON]’s algorithm [24]; (**b**) Th-FxLMS algorithm [25]; (**c**) modified FxLMK algorithm; (**d**) INSS-FxLMS algorithm [26]; (**e**) effect of step-size parameter in the proposed FxLMS-based impulsive ANC algorithms; (**f**) performance comparison between various algorithms: (1) [PERSON]’s algorithm (\(\mu=1\times 10^{-8}\)); (2) Th-FxLMS algorithm (\(\mu=1\times 10^{-5}\)); (3) modified FxLMK algorithm (\(\mu=1\times 10^{-4}\)); (4) INSS-FxLMS algorithm (\(\mu=5\times 10^{-4}\)); (5) previous CCSS-NMFxLMS algorithm (\(\mu_{1}=1.0,\mu_{2}=1\times 10^{-2}\)); (6) FSS-MFxRRLS algorithm (\(\mu=0.1\)); (7) proposed CCSS-MFxRRLS (\(\mu_{1}=1.0,\mu_{2}=0.05\)). hereby show improved performance as compared with the previous experiments. Nevertheless, the FSS-based algorithms have a trade-off situation for the selection of appropriate step-size parameters. The proposed CCSS-MFxRRLS algorithm does not suffer from such a trade-off issue and achieves the best performance.