The purpose of this thesis is to implement algorithms of matrix eigendecomposition and pseudoinverse computation on a Field Programmable Gate Array (FPGA) platform. Firstly, there are described matrix decomposition methods that are broadly used in mentioned algorithms. Next section is focused on the basic theory and methods of computation eigenvalues and eigenvectors as well as matrix pseudoinverse. Several examples of implementation using Matlab are attached. The Vivado High-Level Synthesis tools and libraries were used for final implementation. After the brief introduction into the FPGA fundamentals the thesis continues with a description of implemented blocks. The results of each variant were compared in terms of timing and FPGA utilization. The selected block has been validated on the development board and its arithmetic precision was analyzed.

Many capacity and near-capacity achieving methods in multiple-input-multipleoutput (MIMO) wireless channels make use of the singular value decomposition (SVD) of the channel matrix. For the multi-user case, the SVD of the channel matrix for each user may result in right and left singular vectors that are similar between users. This proposes another descriptive characterization of the multi-user MIMO channel. Closely aligned singular vectors between any two users could reduce the achievable signaling rates of signal processing communication methods as one user would be more difficult to resolve in space-time from another. An examination into how this alignment can be described in realistic MIMO multipath channels using a two ring channel model is presented. The effects of correlation between singular vectors on achievable signaling rates is shown for one existing algorithm that approaches the sum capacity known as block-diagonalization. Analyzed is actual data collected in several indoor and outdoor experiments performed using newly constructed measurement hardware that extends the capabilities of an existing MIMO measurement system.