MATLAB Implementation of Multi-User Detection: Comparative Analysis of Decorrelating, MMSE, and Conventional Approaches

In this paper, we explore various multi-user detection methodologies, specifically comparing decorrelating detection, minimum mean square error (MMSE) detection, and conventional detection approaches. We analyze the advantages and limitations of each technique while discussing their practical applicability and performance in real-world scenarios. The implementation typically involves MATLAB's signal processing toolbox functions for correlation matrix computation (using corr or xcorr functions) and matrix inversion operations (via inv or pseudo-inverse pinv) for detector formulation. For decorrelating detectors, we focus on zero-forcing algorithms that require inverse correlation matrix calculations, while MMSE implementations incorporate noise variance estimation using techniques like var or awgn functions for simulating channel conditions. Traditional detection methods often employ matched filter banks implemented through convolution operations (conv) or FIR filter designs. We further examine future research directions and development potential in multi-user detection systems, providing valuable insights and references for researchers in related fields. Performance metrics such as bit error rate (BER) calculations using berawgn and system capacity evaluations are incorporated to quantitatively compare detection strategies.