Abstract
In recent years, multiple-input multiple-output (MIMO) technology has attracted attention in wireless communications, because it offers significant increases in data throughput and link range without additional bandwidth. One of the MIMO techniques uses the pre-coding to adjust the transmitted signal to improve the data reliability and the most widely used technique today is using singular value decomposition (SVD) to generate the pre-coding matrix. However, the computational complexity of SVD is significantly high due to the matrix multiplications and iterative characteristic. In this thesis, we present a new element-based iterative, high accurate and low cost singular value decomposition (SVD) algorithm. A new element-based iterative algorithm which with less iterative number of matrix multiplication than QR-based SVD algorithm and matrix's multiplication-based iterative algorithm. The proposed SVD algorithm can be separated into three stages. They are preprocessing, eigenvalue approximation by law of inertia (EALOI) and computing the eigenvectors with tri-diagonal matrix algorithm (CETDMA). The proposed SVD is scalable architecture, so that means a 16X16 SVD can combined by results of 8X8 SVD. Saving 70% complexity in 16X16 matrix compared with QR-based SVD. Based on element's iterative multiplication reduces the complexity efficiently, which support a larger size matrix with 8X8 and 16X16. We verify the proposed design by using the VLSI implementation with CMOS 90 nm technology. The post-layout result of the design has the feature of 0.887X0.887 (mm^2) core area and 22.9mW power consumption at 100MHz operating frequency. Our design can achieve 137k channel matrices (8X8)/s, and NMSE can achieve 10^(-4) and can be extended to deal with different transmit and receive antenna sets.