An Adaptive Beamforming by a Generalized Unstructured Neural Network
In this paper, an adaptive array beamforming by an unstructured neural network based on the mathematics of holographic storage is presented. This work is inspired by similarities between brain waves and the wave propagation and subsequent interference patterns seen in holograms. Then the mathematics to produce a general mathematical description of the holographic process is analyzed. From this analysis it is shown that how the holographic process can be used as an associative memory network. Additionally, the process may also be used a regular feed-forward network. The most striking aspect of these network is that, using the holographic process, the apriori knowledge of the system may be better utilized to tailor the neural network for an adaptive beamforming problem. This aspect, makes this neural network formation process particularly useful for the beamforming.
A. Demirkol et al., "An Adaptive Beamforming by a Generalized Unstructured Neural Network," Proceedings of the 13th International Conference on Neural Information Processing (2006, Hong Kong, China), vol. 4233 LNCS - II, pp. 543-552, Springer Verlag, Oct 2006.
The definitive version is available at https://doi.org/10.1007/11893257
13th International Conference on Neural Information Processing, ICONIP 2006 (2006: Oct. 3-6, Hong Kong, China)
Electrical and Computer Engineering
Keywords and Phrases
Green's Functions; Adaptive Beamforming; Feed-Forward Neural Network; Holographic Processing; Radial Basis Functions; Wave Propagation; Neural networks (Computer science)
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2006 Springer Verlag, All rights reserved.
01 Oct 2006