Performance Comparison of Linear Decorrelating Multiuser Detection and Minimum Mean Square Error Multiuser Detection in DS-Spread Spectrum Systems

In Direct-Sequence Spread Spectrum (DS-SS) systems, noise is typically modeled as additive white Gaussian noise (AWGN). This study compares the performance of traditional single-user detection with two advanced multiuser detection approaches: linear decorrelating multiuser detection and minimum mean square error (MMSE) multiuser detection. These algorithms effectively identify and separate signals from multiple users in noisy environments through distinct mathematical approaches. The linear decorrelating detector employs a zero-forcing strategy that completely eliminates multiple-access interference by inverting the cross-correlation matrix of user spreading codes. Implementation typically involves calculating the pseudo-inverse of the signature matrix using MATLAB's pinv() function or similar linear algebra operations. Meanwhile, the MMSE detector achieves a balance between interference suppression and noise enhancement by incorporating the noise variance into its weighting matrix calculation, often implemented through regularized matrix inversion techniques. In DS-SS systems, the presence of noise significantly impacts system performance, making the selection of appropriate detection methods crucial for performance optimization. Code implementation considerations include proper handling of correlation matrices, SNR estimation for MMSE adaptation, and computational complexity analysis for real-time applications. The comparative analysis evaluates these methods through key metrics like bit error rate performance, near-far resistance, and computational efficiency under varying noise conditions.