A generalized matrix-decomposition processor for joint MIMO transceiver design
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Date Issued
2017
Author(s)
Wu, Yu-Chi
Abstract
A generalized matrix-decomposition processor is designed and implemented, which supports QR decomposition (QRD), eigenvalue decomposition (EVD), and geometric-mean decomposition (GMD), to accelerate computations in MIMO precoding/beamforming systems. The processor adopts memory-based architecture with 16 processing elements (PEs) each consisting of one CORDIC module. An improved GMD algorithm is proposed, which reduces 13.2% complexity and can be implemented by homogeneous CORDIC operations. The EVD adopts the Rayleigh quotient shift and deflation technique to accelerate convergence. The basis computations can be accomplished by mirrored operations during channel matrix decomposition. From the implementation results, the generalized processor achieves decomposition throughput of 10M, 0.99M, 2.96M matrixes per second for 4 × 4 complex QRD, EVD and GMD. © 2017 IEEE.
Event(s)
2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Subjects
EVD
GMD
MIMO precoding
QRD
SVD
Description
New Orleans, 5 March 2017 through 9 March 2017
Type
conference paper