In this letter, we propose a low complexity maximum likelihood (ML) decoding algorithm for orthogonal spacetime block codes (OSTBCs) based on the real-valued lattice representation and QR decomposition.We show that for a system with rate r = K/T, where K is the number of transmitted symbols per T time slots, the proposed algorithm decomposes the original complex-valued system into a parallel system represented by 2K real-valued components, thus allowing for a simple and independent detection of the real and imaginary parts of each complex transmitted symbol. We further show that for square L-QAM constellations, the proposed algorithm reduces the decoding computational complexity from O(L) for conventional ML to O(radicL) without sacrificing the performance.

In this letter, we propose a low complexity Maximum Likelihood (ML) decoding algorithm for quasi-orthogonal space-time block codes (QOSTBCs) based on the real-valued lattice representation and QR decomposition. We show that for a system with rate r = ns/T, where nsis the number of transmitted symbols per T time slots; the proposed algorithm decomposes the original complex-valued system into a parallel system with ns2 times 2 real-valued components, thus allowing for a simple joint decoding of two real symbols. For a square QAM constellation with L points (L-QAM), this algorithm achieves full diversity by properly incorporating two-dimensional rotation using the optimal rotation angle and the same rotating matrix for any number of transmit antennas (N ges 4). We show that the complexity gain becomes greater when N or L becomes larger. The complexity of the proposed algorithm is shown to be linear with the number of transmitted symbols ns.

In this letter, we propose a reordering of the channel representation for Sphere Decoding (SD) where the real and imaginary parts of each jointly detected symbol are decoded independently. Making use of the proposed structure along with a scalar quantization technique, we reduce the decoding complexity substantially. We show that this approach achieves 85% reduction in the overall complexity compared to the conventional SD for a 2 times 2 system, and 92% reduction for the 4 times 4 and 6 times 6 cases at low SNR values, and almost 50% at high SNR, thus relaxing the requirements for hardware implementation.

The complexity of sphere decoding (SD) has been widely studied due to the importance of this algorithm in obtaining the optimal maximum likelihood (ML) performance with lower complexity. In this paper, we propose a proper tree search traversal technique that reduces the overall SD computational complexity without sacrificing the performance. We exploit the similarity among the complex symbols in a square QAM lattice representation and rewrite the squared norm ML metric in a simpler form allowing significant reduction of the number of operations required to decode the transmitted symbols. We also show that this approach achieves > 45% complexity gain for systems employing 4-QAM, and that this gain becomes bigger as the constellation size is larger.

In this paper, we propose a new decoding algorithm for quasiorthogonal space-time block codes (QOSTBCs) which achieves near maximum likelihood (ML) performance while substantially reducing the decoding complexity. We show that for a system with rate r = ns/T, where nsis the number of transmitted symbols per T time slots; the proposed algorithm decomposes the original complex-valued system into a parallel system with ns2times2 real-valued components, thus allowing for a simple decoding of one real symbol at a time. For a square QAM constellation with L points (L-QAM), this algorithm achieves full diversity by properly incorporating two-dimensional rotation using the optimal rotation angle and the same rotating matrix for any number of transmit antennas (Nges4). We show that the complexity of this algorithm is linear with the number of transmitted symbols ns, and is independent of the constellation size L.

In this paper, we propose a low complexity Maximum Likelihood (ML) decoding algorithm for orthogonal space-time block codes (OSTBCs) based on the real-valued lattice representation and QR decomposition. We show that for a system with rate r = K/T, where K is the number of transmitted symbols per T time slots; the proposed algorithm decomposes the original complex-valued system into a parallel system represented by 2 K real-valued components, thus allowing for a simple and independent detection of the real and imaginary parts of each complex transmitted symbol. We further show that for a system employing the well-known Alamouti OSTBC and 16-QAM modulation scheme, the new approach achieves >82% reduction in the overall complexity compared to conventional ML detection. Moreover, we show that for square L-QAM constellations, the proposed algorithm reduces the decoding computational complexity from O(L) for conventional ML to O(radicL) without sacrificing the performance.

In this paper, we propose a low complexity Maximum Likelihood (ML) decoding algorithm for quasi-orthogonal space-time block codes (QOSTBCs) based on the real-valued lattice representation and QR decomposition. We show that for a system with rate r = ns/T, where ns is the number of transmitted symbols per T time slots; the proposed algorithm decomposes the original complex-valued system into a parallel system with ns 2 x 2 real-valued components, thus allowing for a simple joint decoding of two real symbols. For a square QAM constellation with L points (L-QAM), this algorithm achieves full diversity by properly incorporating two-dimensional rotation using the optimal rotation angle and the same rotating matrix for any number of transmit antennas (N ges 4). We further show that for N = 8 and 16-QAM modulation scheme, the new approach achieves > 98% reduction in the overall complexity compared to conventional ML detection, and > 93% reduction compared to the most competitive reported algorithms in the literature. This complexity gain becomes greater when N or L becomes larger. We also show that the complexity of the proposed algorithm is linear with L and ns.

Sphere decoding (SD) is a low complexity maximum likelihood (ML) detection algorithm, which has been adapted for different linear channels in digital communications. The complexity of SD has been shown to be exponential in some cases, and polynomial in others and under certain assumptions. The sphere radius and the number of nodes visited throughout the tree traversal search are the decisive factors for the complexity of the algorithm. The radius problem has been addressed and treated widely in the literature. In this paper, we propose a new structure for SD, which drastically reduces the overall complexity. The complexity is measured in terms of the floating point operations per second (FLOPS) and the number of nodes visited throughout the algorithm's tree search. This reduction in complexity is due to the ability of decoding the real and imaginary parts of every jointly detected symbol independently of each other, making use of the new lattice representation. We further show by simulations that the new approach achieves 80% reduction in the overall complexity compared to the conventional SD for a 2times2 system, and almost 50% reduction for the 4times4 and 6times6 cases, thus relaxing the requirements for hardware implementation.