Inverse System Identification, Channel Identification, and LMMSE Estimation

Based on the detailed information from the adaptive filtering course project and its report, we successfully implemented inverse system identification and channel identification using both LMS and RLS algorithms under three distinct ISI channel conditions, along with LMMSE estimation. The LMS algorithm employs a gradient descent approach with low computational complexity, making it suitable for real-time applications, while the RLS algorithm utilizes a recursive least squares method with faster convergence at the cost of higher computational load. For LMMSE estimation, we applied a linear minimum mean square error estimator to minimize estimation errors in the presence of channel noise. These algorithms effectively mitigate interference and noise in the channel, significantly enhancing signal quality and reliability. Our findings demonstrate the strong application potential of adaptive filtering in channel communication and provide valuable insights for future research.