Abstract
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.
Highlights
Multiple-input multiple-output (MIMO) and its extension to very large arrays have been a trending topic of research in the past few years
In this letter, capitalizing on the results obtained so far in the literature with the Neumann series, we propose and evaluate methods based on the matrix inversion lemma to update the inverse, when a user is added or removed from the system
Using the fact that in massive multiple-input multiple-output (MIMO) systems Z is an almost diagonal matrix, a hardware-efficient method based on the Neumann series was first proposed in [5] to approximate the required inverse in (2)
Summary
Energy constrained applications, such as base stations (BS) and full-duplex relays, can effectively take advantage of this fact to serve a large number of users simultaneously [3], [4]. Without compromising the performance of the system, low complexity methods to retrieve the information from the received signal should be looked for For these reasons, [5] recently proposed the use of the Neumann series for a fast and efficient approximate inversion method of matrices. All of the proposed algorithms require a low number of calculations and are memory transfer friendly This reduction in computation time might be specially useful in slow fading environments with a dynamic set of active of users, and in fast fading channels where new channel estimations are obtained very frequently. Using the Neumann series inverse approximation as the input, the impact of the algorithms in the bit error rate (BER) is evaluated and compared via numerical simulations
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