Fast Matrix Inversion Updates for Massive MIMO Detection and Precoding

In this letter, methods and corresponding complexities for fast matrix inversion updates in the context of massive multiple-input multiple-output (MIMO) are studied. In particular, we propose an on-the-fly method to recompute the zero forcing (ZF) filter when a user is added or removed from the system. Additionally, we evaluate the recalculation of the inverse matrix after a new channel estimation is obtained for a given user. Results are evaluated numerically in terms of bit error rate (BER) using the Neumann series approximation as the initial inverse matrix. It is concluded that, with fewer operations, the performance after an update remains close to the initial one.

I. Introduction

MULTIPLE-input multiple-output (MIMO) and its extension to very large arrays have been a trending topic of research in the past few years. The theoretical advantages of massive MIMO systems are clear: increased spectral capacity while attaining high energy efficiencies [1]. However, with the increase of the number of dimensions, using conventional MIMO algorithms may not be suitable any more in terms of computational efficiency and new methods must emerge.