SVD Decomposition in MIMO Precoding
- Login to Download
- 1 Credits
Resource Overview
Detailed Documentation
In MIMO precoding, Singular Value Decomposition (SVD) serves as a fundamental method primarily evaluated through Bit Error Rate (BER) performance metrics. This technique decomposes the input signal matrix into singular value matrices and right singular vector matrices, enabling optimized signal transmission. Code implementations typically involve linear algebra libraries (e.g., MATLAB's svd() function or NumPy's numpy.linalg.svd) to factorize the channel matrix H into U, Σ, and V* components. The diagonal singular value matrix Σ facilitates power allocation across spatial streams, while the unitary matrices enable precoding and combining operations. By leveraging SVD decomposition, systems achieve maximized transmission efficiency through water-filling algorithms and enhanced data reliability via eigenmode transmission. Consequently, SVD decomposition plays a critical role in MIMO precoding architectures and is essential for advancing communication system performance.
- Login to Download
- 1 Credits