Performance Analysis of Filter-Based Multiuser Detectors

This study compares the performance of multiuser detectors based on LMS (Least Mean Squares) algorithm, RLS (Recursive Least Squares) algorithm, and Kalman filter implementations. Building upon existing research, we conduct an in-depth analysis of these algorithms' advantages and limitations across various operational scenarios. The LMS detector typically employs stochastic gradient descent with code implementations focusing on step-size parameter adaptation and real-time coefficient updates. RLS-based detectors implement matrix inversion operations using efficient recursive formulations, exhibiting superior convergence through exponential weighting mechanisms. Kalman filter detectors utilize state-space modeling with prediction-correction cycles that maintain optimal estimation under Gaussian assumptions. Our investigation further examines their stability and robustness in noisy environments, analyzing how each algorithm handles channel uncertainties and interference patterns. We evaluate their multiuser interference suppression capabilities through computational complexity assessments and bit error rate (BER) performance metrics. The experimental framework involves MATLAB simulations with direct matrix operations for RLS, iterative weight updates for LMS, and recursive covariance computations for Kalman implementations. Through comprehensive research and experimental validation, we aim to provide accurate performance benchmarks that support informed selection and practical application of multiuser detectors in communication systems.