In addition to other efficient schemes, large scale antenna arrays are expected to support future communication systems required throughputs. The main challenge of these systems is to find low complexity, hardware feasible algorithms that are able to attain near optimal performance.
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.