Abstract
The maximum-likelihood (ML) adaptive weight vector is derived, and its performance is compared with those of the sample matrix inversion (SMI) method and the least-mean-square (LMS) algorithm. The ML adaptive weight vector and the SMI method achieve identical convergence rates for the average SNR. With the desired signal absent, they are superior to the LMS algorithm. However, when the desired signal is present and the optimum SNR which depends on the received SNR is large, they lose their superiority. For the average MSE performance, the convergence of the ML adaptive weights is the fastest when the optimum SNR is high enough. With a small optimum SNR, the SMI method performs better than the other algorithms. >
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
