Dominik Seethaler

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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)
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)
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)
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)
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 logsum(More)
Lattice-reduction (LR)-aided successive interference cancellation (SIC) is able to achieve close-to optimum error-rate performance for data detection in multiple-input multiple-output (MIMO) wireless communication systems. In this work, we propose a hardware-efficient VLSI architecture of the LenstraLenstra-Lovász (LLL) LR algorithm for SIC-based data(More)
We consider the downlink of a multiuser system with multiple antennas at the base station. Vector perturbation (VP) precoding is a promising variant of transmit-side channel inversion allowing the users to detect their data in a simple, noncooperative manner. VP precoding has so far been developed and analyzed under the assumptions that the transmitter has(More)
Recently the use of lattice reduction (LR) methods for data detection in multiple-input multiple-output (MIMO) systems has been proposed in order to achieve full diversity with suboptimal detection schemes. To this end, several reduction criteria and algorithms known from lattice theory have been applied. In this work new insights about the applicability of(More)
Channel estimation is an important and challenging task in MIMO communications. The minimum mean-square-error (MMSE) channel estimator is able to exploit spatial correlation of the MIMO channel but requires prior estimation of the channel correlation matrix. In this paper, we investigate pilot-based MMSE channel estimation including channel correlation(More)
Lattice 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)