Lattice-reduction aided HNN for vector precoding
Gardasevic V, Müller R, Ryan D, Lundheim L, Øien G (2010)
Publication Language: English
Publication Status: Published
Publication Type: Conference contribution, Conference Contribution
Publication year: 2010
Pages Range: 37-41
Article Number: 5649344
Event location: Taichung
ISBN: 9781424460175
DOI: 10.1109/ISITA.2010.5649344
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.
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Norway (NO)How to cite
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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