Accelerated Randomized Methods for Receiver Design in Extra-Large Scale MIMO Arrays

TL;DR

This paper introduces three accelerated randomized Kaczmarz algorithms for XL-MIMO receiver design, improving robustness against interference and sparse channels, and approximating zero-forcing schemes with low complexity.

Contribution

The paper proposes three novel accelerated RK-based receiver algorithms tailored for XL-MIMO, enhancing performance and robustness over existing methods.

Findings

Outperform previous schemes in XL-MIMO scenarios.

More robust against inter-user interference and sparse channels.

Approximate RZF scheme using low-complexity RK-based methods.

Abstract

Massive multiple-input-multiple-output (M-MIMO) features a capability for spatial multiplexing of large number of users. This number becomes even more extreme in extra-large (XL-MIMO), a variant of M-MIMO where the antenna array is of very large size. Yet, the problem of signal processing complexity in M-MIMO is further exacerbated by the XL size of the array. The basic processing problem boils down to a sparse system of linear equations that can be addressed by the randomized Kaczmarz (RK) algorithm. This algorithm has recently been applied to devise low-complexity M-MIMO receivers; however, it is limited by the fact that certain configurations of the linear equations may significantly deteriorate the performance of the RK algorithm. In this paper, we embrace the interest in accelerated RK algorithms and introduce three new RK-based low-complexity receiver designs. In our experiments, our methods are not only able to overcome the previous scheme, but they are more robust against inter-user interference (IUI) and sparse channel matrices arising in the XL-MIMO regime. In addition, we show that the RK-based schemes use a mechanism similar to that used by successive interference cancellation (SIC) receivers to approximate the regularized zero-forcing (RZF) scheme.