Subspace-Based Channel Estimation for Single-Input Multiple-Output OFDM Systems

This document presents a subspace-based channel estimation technique designed for direct implementation in single-input multiple-output (SIMO) OFDM systems. The technique employs eigenvalue decomposition of the received signal covariance matrix to separate signal and noise subspaces, enabling more accurate channel state information (CSI) estimation. Key implementation aspects include: pilot symbol arrangement for covariance matrix construction, singular value decomposition (SVD) for subspace identification, and least-squares estimation for channel coefficients. By utilizing this method, we effectively mitigate channel fading impacts on system performance while providing more reliable communication. The algorithm reduces estimation error through optimal subspace projection and noise suppression techniques. Furthermore, this approach simplifies system design complexity by eliminating the need for complex training sequences, making systems easier to deploy and maintain. The implementation typically involves MATLAB functions like svd() for matrix decomposition and pinv() for pseudo-inverse calculations. Overall, the subspace-based channel estimation technique offers a robust and efficient solution for SIMO-OFDM systems, achieving performance improvements through mathematical optimization of signal subspace properties while maintaining computational efficiency through matrix operations.