Low-complexity approximate iterative LMMSE detection for large-scale MIMO systems

Authors

Xiaochen He, University of WollongongFollow
Qinghua Guo, University of WollongongFollow
Jun Tong, University of WollongongFollow
Jiangtao Xi, University of WollongongFollow
Yanguang Yu, University of WollongongFollow

RIS ID

114537

Publication Details

X. He, Q. Guo, J. Tong, J. Xi & Y. Yu, "Low-complexity approximate iterative LMMSE detection for large-scale MIMO systems," Digital Signal Processing, vol. 60, pp. 134-139, 2017.

Abstract

This paper deals with iterative detection for uplink large-scale MIMO systems. The well-known iterative linear minimum mean squared error (LMMSE) detector requires quadratic complexity (per symbol per iteration) with the number of antennas, which may be a concern in large-scale MIMO. In this work, we develop approximate iterative LMMSE detectors based on transformed system models where the transformation matrices are obtained through channel matrix decompositions. It is shown that, with quasi-linear complexity (per symbol per iteration), the proposed detectors can achieve almost the same performance as the conventional LMMSE detector. It is worth mentioning that the linear transformations are also useful to reduce the complexity of downlink precoding, so the relevant computational complexity can be shared by both uplink and downlink.