Proceedings Article10.1109/PIMRC.2010.5671711
Augmented lattice reduction for low-complexity MIMO decoding
Laura Luzzi,Ghaya Rekaya-Ben Othman,Jean-Claude Belfiore +2 more
- 17 Dec 2010
- pp 235-240
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TL;DR: It is proved that augmented lattice reduction attains the maximum receive diversity order of the channel; simulation results evidence that it significantly outperforms LLL-SIC detection without entailing any additional complexity.
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Abstract: Lattice reduction algorithms, such as the LLL algorithm, have been proposed as preprocessing tools in order to enhance the performance of suboptimal receivers in MIMO communications. In this paper we introduce a new kind of lattice reduction-aided decoding technique, called augmented lattice reduction, which recovers the transmitted vector directly from the change of basis matrix, and therefore doesn't entail the computation of the pseudo-inverse of the channel matrix or its QR decomposition. We prove that augmented lattice reduction attains the maximum receive diversity order of the channel; simulation results evidence that it significantly outperforms LLL-SIC detection without entailing any additional complexity.
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Citations
Lattice Gaussian Sampling by Markov Chain Monte Carlo: Bounded Distance Decoding and Trapdoor Sampling
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TL;DR: The Markov chain Monte Carlo (MCMC)-based sampling technique is advanced in several fronts, revealing a flexible trade-off between the decoding radius and complexity and the independent multiple-try Metropolis-Klein algorithm is proposed to enhance the convergence rate.
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Decoding by Sampling — Part II: Derandomization and Soft-Output Decoding
Zheng Wang,Shuiyin Liu,Cong Ling +2 more
TL;DR: It is demonstrated that the derandomized sampling algorithm is capable of achieving near-maximum a posteriori (MAP) performance, and Simulation results show that near-optimum performance can be achieved by a moderate size K in both lattice decoding and soft-output decoding.
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Lattice Gaussian Sampling by Markov Chain Monte Carlo: Convergence Rate and Decoding Complexity.
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TL;DR: Decoding by MCMC-based lattice Gaussian sampling is investigated in full details, revealing a flexible trade-off between the decoding performance and complexity.
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Derandomized sampling algorithm for lattice decoding
Zheng Wang,Cong Ling +1 more
- 01 Sep 2012
TL;DR: A derandomized algorithm of sampling decoding is proposed with further performance improvement and complexity reduction.
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Lattice Gaussian Sampling by Markov Chain Monte Carlo: Bounded Distance Decoding and Trapdoor Sampling
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TL;DR: In this article, Markov chain Monte Carlo (MCMCMC)-based sampling technique is applied to trapdoor sampling, revealing a flexible trade-off between the decoding radius and complexity.
1
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