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—Multiple-input multiple-output (MIMO) technology is the key to meet the demands for data rate and link reliability of modern wireless communication systems, such as IEEE 802.11n or 3GPP-LTE. The full potential of MIMO systems can, however, only be achieved by means iterative MIMO decoding relying on soft-input soft-output (SISO) data detection. In this(More)
—It is well known that suboptimal detection schemes for multiple-input multiple-output (MIMO) spatial multiplexing systems (equalization-based schemes as well as nulling-and-cancelling schemes) are unable to exploit all of the available diversity , and thus, their performance is inferior to ML detection. Motivated by experimental evidence that this inferior(More)
— In bit-interleaved coded modulation (BICM) systems employing maximum-likelihood decoding, a demodulator (demapper) calculates a log-likelihood ratio (LLR) for each coded bit, which is then used as a bit metric for Viterbi decoding. In the MIMO case, the computational complexity of LLR calculation tends to be excessively high, even if the log-sum(More)
Approximate vector perturbation techniques assisted by LLL lattice reduction (LR) can exploit all the diversity that is available in multiuser multi-antenna broadcast systems. However, the required computational complexity of LLL-LR can be quite large. In this paper, we propose a much simpler and much more efficient LR algorithm than LLL. This LR technique(More)
— We discuss and compare the most important detection techniques for MIMO spatial multiplexing wireless systems, focusing on their performance and computational complexity. Our analysis shows that the limited performance of conventional suboptimal detection techniques is primarily caused by their inability to cope with poorly conditioned channels. The(More)
Lattice reduction by means of the LLL algorithm has been previously suggested as a powerful preprocessing tool that allows to improve the performance of suboptimal detectors and to reduce the complexity of optimal MIMO detectors. The complexity of the LLL algorithm is often cited as polynomial in the dimension of the lattice. In this paper we argue that(More)
L attice reduction is a powerful concept for solving diverse problems involving point lattices. Signal processing applications where lattice reduction has been successfully used include global positioning system (GPS), frequency estimation, color space estimation in JPEG pictures, and particularly data detection and precoding in wireless communication(More)
Lattice reduction (LR) is a powerful technique for improving the performance of suboptimum MIMO data detection methods. For LR-assisted data detection, the LLL algorithm has been considered almost exclusively so far. In this paper, we propose and develop the application of Seysen's algorithm (SA) to LR-assisted MIMO detection , and we show that the SA is a(More)