Lattice-reduction aided HNN for vector precoding

Beitrag bei einer Tagung
(Konferenzbeitrag)


Details zur Publikation

Autorinnen und Autoren: Gardasevic V, Müller R, Ryan D, Lundheim L, Øien G
Jahr der Veröffentlichung: 2010
Seitenbereich: 37-41
ISBN: 9781424460175
Sprache: Englisch


Abstract

In this paper we propose a modification of the Hopfield neural networks for vector precoding, based on Lenstra, Lenstra, and Lovàsz 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. © 2010 IEEE.


FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Müller, Ralf Prof. Dr.-Ing.
Professur für Informationsübertragung


Einrichtungen weiterer Autorinnen und Autoren

NTNU Trondheim - Norwegian University of Science and Technology


Zitierweisen

APA:
Gardasevic, V., Müller, R., Ryan, D., Lundheim, L., & Øien, G. (2010). Lattice-reduction aided HNN for vector precoding. In Proceedings of the 2010 20th International Symposium on Information Theory and Its Applications, ISITA 2010 and the 2010 20th International Symposium on Spread Spectrum Techniques and Applications, ISSSTA 2010 (pp. 37-41). Taichung.

MLA:
Gardasevic, Vesna, et al. "Lattice-reduction aided HNN for vector precoding." Proceedings of the 2010 20th International Symposium on Information Theory and Its Applications, ISITA 2010 and the 2010 20th International Symposium on Spread Spectrum Techniques and Applications, ISSSTA 2010, Taichung 2010. 37-41.

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