Recursive least square and least means square equalizers optimization algorithms in Rayleigh fading

• Main Authors: Dela Cruz, Arianne Grace, Cajayon, Camilo, Luna, Joseph Jay, Tomboc, Cris Edward
• Format: text
• Published: Animo Repository 2019
• Subjects: Least squares; Adaptive filters; Rayleigh model; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/4224
• Institution: De La Salle University

Recursive Least Squares (RLS) are adaptive filters that search for the coefficient weights that are set to minimize the weighted linear least square cost function of the signal that is inputted. In the RLS derivation, the input signals are known to be deterministic. This method provides fast convergence but its drawback is the high cost of computational complexity. On the other hand, the Least Means Square algorithm is used to mimic the desired filter by searching for its filter coefficients which relate to producing the least means square of the error signal. This method uses a stochastic gradient descent method in the filter. This research will develop a Recursive Least Square and Least Means Square Equalizers Optimization Algorithms in Rayleigh Fading. Testing of the system will be done by using the Matlab Simulink. © 2019, World Academy of Research in Science and Engineering. All rights reserved.