This work presents a high-throughput and low-latency Index Terms-matrix inversion design for a linear pre-coder for massive MIMO systems. Because of the large number of Base Station (BS) antennas as well as the multiple User Terminals (UTs) served in a massive MIMO system, the channel matrix dimensions become larger. Inversions of such large matrices using direct inversion methods, such as used in linear pre-coders like Zero Forcing (ZF), would entail prohibitive complexity. For avoiding such complexity, Neumann series based approximate inversion has been suggested for linear pre-coders in massive MIMO systems. However the performance, complexity and convergence speed of the Neumann series approach depends very much on the initial approximation of the inverse used as a starting point. In this work, we present a novel initial approximation for the Neumann series which facilitates the parallel computation of the inverse and hence results in lower latency for inversion as well as better accuracy when compared to the previous approaches. A VLSI architecture of the proposed method is implemented for the inversion of a 16 × 16 matrix, in TSMC 65nm technology. A throughput of 0.54M to 15M matrix inversion per sec is achieved at a clock frequency of 460MHz with a 117K gate count.
['Syed Mohsin Abbas', 'Chi-Ying Tsui']
Low-latency approximate matrix inversion for high-throughput linear pre-coders in massive MIMO