Enlarged QR-LRL based SM-MIMO detector for efficient soft output generation

TLDR: An enlargement of candidate vector set of QR-LRL based MIMO detector for efficient soft output generation is proposed in this article, which effectively removes EVS problem and achieves soft output ML performance while keeping the computation complexity low, especially at low modulation order.

Abstract: An enlargement of candidate vector set of QR-LRL (QR — Least Reliable Layer) based MIMO detector for efficient soft output generation is proposed. Previous work [8] shows that QR-LRL based MIMO detector approaches hard decision output ML performance, but does not match the soft output ML performance due to empty candidate vector set problem. Performance degradation is more severe when modulation order is low. Some of the previous methods have provided solutions to mitigate Empty Vector Set (EVS) problem [4] [8] [9], but are not efficient in terms of performance or computation complexity. In this paper, we enlarged candidate vector set of QR-LRL detector by applying every constellation point at each layer. The proposed detector thus effectively removes EVS problem and achieves soft ML performance while keeping the computation complexity low, especially at low modulation order.