In-Channel Channel Estimation Implementation

In my program, I focus on implementing in-channel channel estimation techniques. The implementation includes not only various modern channel estimation algorithms such as Least Squares (LS), Minimum Mean Square Error (MMSE), and Maximum Likelihood (ML) estimators, but also provides detailed code-level explanations and performance comparisons. Through these algorithm implementations using MATLAB's signal processing toolbox and custom functions, the program accurately estimates channel characteristics and performance metrics. The core functionality involves processing received signals, calculating channel impulse responses, and evaluating estimation accuracy through error metrics like Mean Square Error (MSE). Additional features include data visualization using plot functions for channel response graphs and constellation diagrams, along with result analysis modules that generate performance reports. Whether for research purposes or practical applications in communication systems, my program offers a comprehensive and accurate channel estimation solution with optimized code structure for better system performance and reliability. The modular design allows easy integration of new algorithms while maintaining efficient memory handling through matrix operations and vectorized computations.