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Neural network detection for satelli...

Queen's University at Kingston (Canada).

Neural network detection for satellite mobile communications.

紀錄類型:	書目-電子資源 : 單行本
正題名/作者:	Neural network detection for satellite mobile communications./
作者:	Yuan, Jun.
面頁冊數:	108 p.
附註:	Source: Masters Abstracts International, Volume: 42-04, page: 1350.
Contained By:	Masters Abstracts International42-04.
標題:	Engineering, Electronics and Electrical. -
電子資源:	Download fulltext (下載全文)
ISBN:	0612862046

Neural network detection for satellite mobile communications.
Yuan, Jun.

Neural network detection for satellite mobile communications. - 108 p.

Source: Masters Abstracts International, Volume: 42-04, page: 1350.

Thesis (M.Sc.(Eng))--Queen's University at Kingston (Canada), 2003.

In the first part of this thesis, we derive generalized formulas to calculate the exact average symbol error rate (SER) of satellite mobile channels. These formulas can be used to evaluate both the effects of high-power amplifier (HPA) nonlinearity and flat fading. In the second part, we propose two adaptive neural network receivers that can be used in conjunction with well-known techniques, like adaptive filters or pilot-symbol-aided method, to overcome the problems of nonlinearity and fading. The fast neural network learning algorithm (i.e. natural gradient descent) is employed, which outperforms the ordinary gradient descent (i.e. back propagation) algorithm in terms of convergence speed and modeling accuracy. Since the neural network is used as a key technology in receiver design to overcome the nonlinear problem caused by HPA, it is important for system designers to understand its learning behavior and performance capabilities. The last part of the thesis, therefore, investigates a statistical analysis of natural gradient neural network learning in the case of finite input elements. (Abstract shortened by UMI.)

ISBN: 0612862046Subjects--Topical Terms:

170927
Engineering, Electronics and Electrical.

Neural network detection for satellite mobile communications.
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520 $a In the first part of this thesis, we derive generalized formulas to calculate the exact average symbol error rate (SER) of satellite mobile channels. These formulas can be used to evaluate both the effects of high-power amplifier (HPA) nonlinearity and flat fading. In the second part, we propose two adaptive neural network receivers that can be used in conjunction with well-known techniques, like adaptive filters or pilot-symbol-aided method, to overcome the problems of nonlinearity and fading. The fast neural network learning algorithm (i.e. natural gradient descent) is employed, which outperforms the ordinary gradient descent (i.e. back propagation) algorithm in terms of convergence speed and modeling accuracy. Since the neural network is used as a key technology in receiver design to overcome the nonlinear problem caused by HPA, it is important for system designers to understand its learning behavior and performance capabilities. The last part of the thesis, therefore, investigates a statistical analysis of natural gradient neural network learning in the case of finite input elements. (Abstract shortened by UMI.)
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