DocumentCode :
3639081
Title :
Lattice-reduction aided HNN for vector precoding
Author :
Vesna Gardašević;Ralf R. Müller;Daniel J. Ryan;Lars Lundheim;Geir E. Øien
Author_Institution :
Department of Electronics and Telecommunications, The Norwegian University of Science and Technology, 7491 Trondheim, Norway
fYear :
2010
Firstpage :
37
Lastpage :
41
Abstract :
In this paper we propose a modification of the Hopfield neural networks for vector precoding, based on Lenstra, Lenstra, and Lovasz lattice basis reduction. This precoding algorithm controls the energy penalty for system loads α = K/N close to 1, with N and K denoting the number of transmit and receive antennas, respectively. Simulation results for the average transmit energy as a function of α show that our algorithm improves performance within the range 0.9 ≤ α ≤ 1, between 0.4 dB and 2.6 dB in comparison to standard HNN precoding. The proposed algorithm performs close to the sphere encoder (SE) while requiring much lower complexity, and thus, can be applied as an efficient suboptimal precoding method.
Keywords :
"Lattices","Receiving antennas","MIMO","Optimization","Computational complexity","Artificial neural networks"
Publisher :
ieee
Conference_Titel :
Information Theory and its Applications (ISITA), 2010 International Symposium on
Print_ISBN :
978-1-4244-6016-8
Type :
conf
DOI :
10.1109/ISITA.2010.5649344
Filename :
5649344
Link To Document :
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