Algorithm study of digital HPA predistortion using one novel memory type BP neural network
Based on the characteristic analysis of the high power amplifier (HPA) in wide-band CMMB repeater stations,a novel neural network was proposed which can respectively process the memory effect and the nonlinear of power amplifier.The novel model based on real-valued time-delay neural networks(RVTDNN)...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2014-01-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.01.003/ |
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| author | Chun-hui HUANG Yong-jie WEN |
| author_facet | Chun-hui HUANG Yong-jie WEN |
| author_sort | Chun-hui HUANG |
| collection | DOAJ |
| description | Based on the characteristic analysis of the high power amplifier (HPA) in wide-band CMMB repeater stations,a novel neural network was proposed which can respectively process the memory effect and the nonlinear of power amplifier.The novel model based on real-valued time-delay neural networks(RVTDNN) uses the Levenberg-Marquardt (LM) optimization to iteratively update the coefficients of the neural network.Due to the new parameters w<sup>0</sup>in the novel NN model,the modified formulas of LM algorithm were provided.Next，in order to eliminate the over-fitting of LM algorithm,the Bayesian regularization algorithm was applied to the predistortion system.Additionally,the predistorter of CMMB repeater stations based on the indirect learning method was constructed to simulate the nonlinearity and memory effect of HPA.Simulation results show that both the NN models can improve system performance and reduce ACEPR (adjacent channel error power ratio ) by about 30 dB.Moreover,with the mean square error less than 10<sup>−6</sup>,the coefficient of network for FIR-NLNNN is about half of that for RVTDNN.Similarly,the times of multiplication and addition in the iterative process of FIR-NLNNN are about 25% of that for RVTDNN. |
| format | Article |
| id | doaj-art-67328425aee748bda0fd36663b230c49 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2014-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-67328425aee748bda0fd36663b230c492025-01-14T06:42:25ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-01-0135162359678598Algorithm study of digital HPA predistortion using one novel memory type BP neural networkChun-hui HUANGYong-jie WENBased on the characteristic analysis of the high power amplifier (HPA) in wide-band CMMB repeater stations,a novel neural network was proposed which can respectively process the memory effect and the nonlinear of power amplifier.The novel model based on real-valued time-delay neural networks(RVTDNN) uses the Levenberg-Marquardt (LM) optimization to iteratively update the coefficients of the neural network.Due to the new parameters w<sup>0</sup>in the novel NN model,the modified formulas of LM algorithm were provided.Next，in order to eliminate the over-fitting of LM algorithm,the Bayesian regularization algorithm was applied to the predistortion system.Additionally,the predistorter of CMMB repeater stations based on the indirect learning method was constructed to simulate the nonlinearity and memory effect of HPA.Simulation results show that both the NN models can improve system performance and reduce ACEPR (adjacent channel error power ratio ) by about 30 dB.Moreover,with the mean square error less than 10<sup>−6</sup>,the coefficient of network for FIR-NLNNN is about half of that for RVTDNN.Similarly,the times of multiplication and addition in the iterative process of FIR-NLNNN are about 25% of that for RVTDNN.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.01.003/HPApredistortionneural networkmemory effectLM algorithmBayesian algorithm |
| spellingShingle | Chun-hui HUANG Yong-jie WEN Algorithm study of digital HPA predistortion using one novel memory type BP neural network Tongxin xuebao HPA predistortion neural network memory effect LM algorithm Bayesian algorithm |
| title | Algorithm study of digital HPA predistortion using one novel memory type BP neural network |
| title_full | Algorithm study of digital HPA predistortion using one novel memory type BP neural network |
| title_fullStr | Algorithm study of digital HPA predistortion using one novel memory type BP neural network |
| title_full_unstemmed | Algorithm study of digital HPA predistortion using one novel memory type BP neural network |
| title_short | Algorithm study of digital HPA predistortion using one novel memory type BP neural network |
| title_sort | algorithm study of digital hpa predistortion using one novel memory type bp neural network |
| topic | HPA predistortion neural network memory effect LM algorithm Bayesian algorithm |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.01.003/ |
| work_keys_str_mv | AT chunhuihuang algorithmstudyofdigitalhpapredistortionusingonenovelmemorytypebpneuralnetwork AT yongjiewen algorithmstudyofdigitalhpapredistortionusingonenovelmemorytypebpneuralnetwork |