Matrix Decomposition Architecture for MIMO Systems: Design and Implementation Trade-offs

Abstract

The singular value decomposition (SVD) and the QR decomposition (QRD) are two prominent matrix decomposition algorithms used in various signal processing applications. In the field of multiple-input multiple-output (MIMO) communication systems, the SVD and the QRD are employed for beamforming and for channel-matrix preprocessing for MIMO detection, respectively. In this paper, we describe a minimum- area matrix decomposition architecture that is programmable to perform QRD and SVD with variable precision and we investigate the associated design and implementation trade-offs. Our reference implementation achieves a hardware efficiency of up to 325 k SVDs/s/mm 2 and 1.92 M QRDs/s/mm 2 for complex-valued 4 times 4-matrices in 0.18 mum CMOS technology.