AI Article Synopsis
- Fractional order algorithms, like the self-adjusting dual-stage LFLMS, outperform traditional algorithms in signal processing while maintaining simplicity.
- LFLMS combines two stages: the first uses the conventional LMS approach, followed by an advanced fractional LMS that enhances precision and speeds up convergence with a slight increase in computational complexity.
- Simulation results reveal that LFLMS achieves a 59% faster convergence rate and reduces mean square error (MSE) by 49% compared to LMS, making it ideal for future wireless technologies such as IoT and 6G.
Article Abstract
Fractional order algorithms demonstrate superior efficacy in signal processing while retaining the same level of implementation simplicity as traditional algorithms. The self-adjusting dual-stage fractional order least mean square algorithm, denoted as LFLMS, is developed to expedite convergence, improve precision, and incurring only a slight increase in computational complexity. The initial segment employs the least mean square (LMS), succeeded by the fractional LMS (FLMS) approach in the subsequent stage. The latter multiplies the LMS output, with a replica of the steering vector (Ŕ) of the intended signal. Mathematical convergence analysis and the mathematical derivation of the proposed approach are provided. Its weight adjustment integrates the conventional integer ordered gradient with a fractional-ordered. Its effectiveness is gauged through the minimization of mean square error (MSE), and thorough comparisons with alternative methods are conducted across various parameters in simulations. Simulation results underscore the superior performance of LFLMS. Notably, the convergence rate of LFLMS surpasses that of LMS by 59%, accompanied by a 49% improvement in MSE relative to LMS. So it is concluded that the LFLMS approach is a suitable choice for next generation wireless networks, including Internet of Things, 6G, radars and satellite communication.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11192369 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0304018 | PLOS |
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