Performance Comparison of Decorrelating, MMSE, and Conventional Detectors

The performance comparison of decorrelating detectors, MMSE detectors, and conventional detectors in communication systems remains a significant research focus in signal processing. These detectors exhibit distinct behaviors in multi-user detection scenarios, particularly when near-far effects and noise interference are present, where performance differences become more pronounced.

The decorrelating detector enhances detection performance by eliminating inter-user interference, making it suitable for multi-user environments. Its key advantage lies in not requiring precise signal power estimation, thus performing well in scenarios with significant near-far effects. However, this detector shows sensitivity to noise, with performance degrading under high-noise conditions. In implementation, decorrelating detection typically involves matrix inversion operations to decorrelate user signals, which can be computationally intensive for large user sets.

The MMSE detector optimizes detection performance under the minimum mean square error criterion, balancing multi-user interference and noise impacts. It demonstrates more stable performance when both near-far effects and noise coexist, though it requires higher computational complexity and accurate power and channel state information. Code implementation often involves adaptive filtering algorithms and recursive calculations to minimize mean square error dynamically.

Conventional detectors (such as matched filters) offer simple implementation but perform poorly in environments with severe multi-user interference. When near-far effects are prominent, strong signals can mask weak signals, leading to rapid performance degradation. These detectors typically use basic correlation operations without interference cancellation mechanisms.

Simulation studies generally include four scenarios: with/without near-far effects and with/without noise. By comparing bit error rates or signal-to-interference-plus-noise ratio performance across these scenarios, detector advantages and limitations become clear. The decorrelating detector excels in strong near-far effect conditions, the MMSE detector shows robustness when noise and interference coexist, while conventional detectors only suit environments with minimal interference and noise. Simulation code typically involves Monte Carlo methods for statistical performance evaluation and matrix operations for detector implementation.

This analysis provides theoretical foundations for detector selection in practical communication systems, particularly in complex wireless environments with multiple users and interference sources. Appropriate detector choice can significantly enhance system performance through optimized algorithm implementation.