Implementation of MLSE Using the Viterbi Algorithm

This MATLAB source code implements Maximum Likelihood Sequence Estimation (MLSE) using the Viterbi algorithm, providing a highly useful solution for sequence estimation problems. The implementation employs the classic Viterbi algorithm, which is widely applied in communication systems and signal processing fields. The code structure includes key components such as state initialization, branch metric calculation, and path metric updates through dynamic programming principles. The algorithm operates by constructing a probabilistic model with state transition matrices to compute the most likely sequence through efficient trellis processing. Core MATLAB functions demonstrate how to handle symbol sequences, calculate cumulative path metrics, and perform traceback operations to determine the optimal path. The implementation advantages include high computational efficiency for processing large datasets and robust performance in noisy environments. Notable code features include: - Systematic state transition handling using matrix operations - Efficient branch metric computation for different modulation schemes - Memory optimization through path history management - Configurable parameters for various channel models This source code serves as an invaluable resource for researchers and engineers working on sequence estimation problems, offering both practical implementation and educational insights into Viterbi algorithm applications. The well-commented code structure allows for easy adaptation to specific system requirements while maintaining algorithmic accuracy.