Optimizing Quasi-Orthogonal STBC Through Group-Constrained Linear Transformation

TL;DR

This paper introduces Group-Constrained Linear Transformation (GCLT) to optimize Quasi-Orthogonal STBCs, reducing decoding complexity while maintaining full diversity and performance comparable to constellation rotation methods.

Contribution

It proposes GCLT as a novel optimization technique for QO-STBCs, deriving optimal parameters and demonstrating improved efficiency and performance.

Findings

GCLT reduces joint detection symbols by half compared to CR-based QO-STBCs.

Optimized GCLT-QO-STBCs achieve full transmit diversity.

Performance loss is negligible compared to CR methods.

Abstract

In this paper, we first derive the generic algebraic structure of a Quasi-Orthogonal STBC (QO-STBC). Next we propose Group-Constrained Linear Transformation (GCLT) as a means to optimize the diversity and coding gains of a QO-STBC with square or rectangular QAM constellations. Compared with QO-STBC with constellation rotation (CR), we show that QO-STBC with GCLT requires only half the number of symbols for joint detection, hence lower maximum-likelihood decoding complexity. We also derive analytically the optimum GCLT parameters for QO-STBC with square QAM constellation. The optimized QO-STBCs with GCLT are able to achieve full transmit diversity, and have negligible performance loss compared with QO-STBCs with CR at the same code rate.