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Lookup NU author(s): Teong Chuah,
Professor Bayan Sharif,
Emeritus Professor Oliver Hinton
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Recently, a robust version of the linear decorrelating detector (LDD) based on the Huber's M-estimation technique has been proposed. In this paper, we first demonstrate the use of a three-layer recurrent neural network (RNN) to implement the LDD without requiring matrix inversion. The key idea is based on minimizing an appropriate computational energy function iteratively. Second, it will be shown that the M-decorrelating detector (MDD) can be implemented by simply incorporating sigmoidal neurons in the first layer of the RNN. A proof of the redundancy of the matrix inversion process is provided and the computational saving in realistic network is highlighted. Third, we illustrate how further performance gain could be achieved for the subspace-based blind MDD by using robust estimates of the signal subspace components in the initial stage. The impulsive noise is modeled using non-Gaussian α-stable distributions, which do not include a Gaussian component but facilitate the use of the recently proposed geometric signal-to-noise ratio (G-SNR). The characteristics and performance of the proposed neural-network detectors are investigated by computer simulation.
Author(s): Hinton OR; Sharif BS; Chuah TC
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Neural Networks
ISSN (print): 1045-9227
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