Reduction of ML decoding complexity for MIMO Sphere Decoding, QOSTBC, and OSTBC

In this paper, we discuss three applications of the QR decomposition algorithm to decoding in a number of Multi-Input Multi-Output (MIMO) systems. In the first application, we propose a new structure for MIMO Sphere Decoding (SD). We show that the new approach achieves 80% reduction in the overall complexity compared to conventional SD for a 2 times 2 system, and almost 50% reduction for the 4 times 4 and 6 times 6 cases. In the second application, we propose a low complexity Maximum Likelihood Decoding (MLD) algorithm for quasi-orthogonal space-time block codes (QOSTBCs). We show that for N = 8 transmit antennas and 16-QAM modulation scheme, the new approach achieves > 97% reduction in the overall complexity compared to conventional MLD, and > 89% reduction compared to the most competitive reported algorithms in the literature. This complexity gain becomes greater when the number of transmit antennas (N) or the constellation size (L) becomes larger. In the third application, we propose a low complexity Maximum Likelihood Decoding (MLD) algorithm for orthogonal space-time block codes (OSTBCs) based on the real-valued lattice representation and QR decomposition. For a system employing the well-known Alamouti OSTBC and 16-QAM modulation scheme, the new approach achieves > 87% reduction in the overall complexity compared to conventional MLD. Moreover, we show that for square L-QAM constellations, the proposed algorithm reduces the decoding computational complexity from O(LN/2) for conventional MLD to O(L) for systems employing QOSTBCs and from O(L) for conventional MLD to O(radicL) for those employing OSTBCs without sacrificing the performance.