Statistical Sparse Decomposition (SSDP) for Underdetermined Blind Source Separation

This MATLAB simulation program implements Statistical Sparse Decomposition (SSDP) for underdetermined blind source separation. It can recover source signals using only observed signals even when the number of receiving antennas is less than the number of source signals. Statistical Sparse Decomposition (SSDP) is a signal processing method that enables source signal recovery through observed signals when the number of receiving antennas is fewer than the source signals. This method has broad applications in wireless communications, particularly for signal separation and reconstruction tasks. The MATLAB simulation implements key algorithmic components including sparse representation using orthogonal matching pursuit, statistical modeling of signal distributions, and optimization techniques for signal recovery. The implementation helps researchers better understand and apply this method through practical code examples demonstrating signal preprocessing, matrix decomposition, and recovery validation procedures.