Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments
In this article, a novel digital predistortion (DPD) model based on complex-valued pipelined Chebyshev functional link recurrent neural network (CPCFLRNN) for joint compensation of wideband transmitter distortions and impairments is proposed. The functional link neural network (FLNN) model has attra...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9184015/ |
| _version_ | 1818917949310763008 |
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| author | Mingyu Li Zhendong Cai Yao Yao Changzhi Xu Yi Jin Xuguang Wang |
| author_facet | Mingyu Li Zhendong Cai Yao Yao Changzhi Xu Yi Jin Xuguang Wang |
| author_sort | Mingyu Li |
| collection | DOAJ |
| description | In this article, a novel digital predistortion (DPD) model based on complex-valued pipelined Chebyshev functional link recurrent neural network (CPCFLRNN) for joint compensation of wideband transmitter distortions and impairments is proposed. The functional link neural network (FLNN) model has attracted much attention from scholars, and many improved models using this structure, such as Chebyshev FLNN, have been applied in the DPD of power amplifiers (PAs). However, these existing neural network models cannot deal with complex-valued input signals simultaneously, and the real-valued model structure will introduce cumbersome training algorithm and result in a long training time. The pipelined recurrent neural network (PRNN) has been successfully applied to nonlinear signal prediction because of its excellent ability for dealing with nonlinear nonstationary signals. Therefore, the PRNN model containing Chebyshev structure is extended to complex domain for the first time to obtain the CPCFLRNN model for DPD application. Considering the strong correlation of in-phase and quadrature phase (I/Q) components of the transmitter signal, the real time recurrent learning (RTRL) algorithm based on fully complex activation function is selected and extended to complex domain to obtain the complex-valued RTRL (CRTRL) algorithm for CPCFLRNN model training. A GaN PA was employed to verify the effectiveness of the proposed models. And the input signal is a 30MHz LTE signals which consists of I/Q imbalance and dc offsets. The experimental results show that the proposed CPCFLRNN model have more accurate modeling effect and better linearization performance compared with the conventional DPD models. |
| first_indexed | 2024-12-20T00:42:11Z |
| format | Article |
| id | doaj.art-d1edbb5f59e84217acd628c724b3608c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T00:42:11Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d1edbb5f59e84217acd628c724b3608c2022-12-21T19:59:32ZengIEEEIEEE Access2169-35362020-01-01815982815983810.1109/ACCESS.2020.30208809184015Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and ImpairmentsMingyu Li0https://orcid.org/0000-0002-9893-3284Zhendong Cai1Yao Yao2Changzhi Xu3Yi Jin4Xuguang Wang5https://orcid.org/0000-0001-9997-155XSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaSchool of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, ChinaState Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing, ChinaXi’an Branch of China Academy of Space Technology, Xi’an, ChinaSchool of Microelectronics and Communication Engineering, Chongqing University, Chongqing, ChinaIn this article, a novel digital predistortion (DPD) model based on complex-valued pipelined Chebyshev functional link recurrent neural network (CPCFLRNN) for joint compensation of wideband transmitter distortions and impairments is proposed. The functional link neural network (FLNN) model has attracted much attention from scholars, and many improved models using this structure, such as Chebyshev FLNN, have been applied in the DPD of power amplifiers (PAs). However, these existing neural network models cannot deal with complex-valued input signals simultaneously, and the real-valued model structure will introduce cumbersome training algorithm and result in a long training time. The pipelined recurrent neural network (PRNN) has been successfully applied to nonlinear signal prediction because of its excellent ability for dealing with nonlinear nonstationary signals. Therefore, the PRNN model containing Chebyshev structure is extended to complex domain for the first time to obtain the CPCFLRNN model for DPD application. Considering the strong correlation of in-phase and quadrature phase (I/Q) components of the transmitter signal, the real time recurrent learning (RTRL) algorithm based on fully complex activation function is selected and extended to complex domain to obtain the complex-valued RTRL (CRTRL) algorithm for CPCFLRNN model training. A GaN PA was employed to verify the effectiveness of the proposed models. And the input signal is a 30MHz LTE signals which consists of I/Q imbalance and dc offsets. The experimental results show that the proposed CPCFLRNN model have more accurate modeling effect and better linearization performance compared with the conventional DPD models.https://ieeexplore.ieee.org/document/9184015/Digital predistortion (DPD)complex-valued pipelined Chebyshev functional link recurrent neural network (CPCFLRNN)I/Q imbalancedc offsetpower amplifier (PA) |
| spellingShingle | Mingyu Li Zhendong Cai Yao Yao Changzhi Xu Yi Jin Xuguang Wang Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments IEEE Access Digital predistortion (DPD) complex-valued pipelined Chebyshev functional link recurrent neural network (CPCFLRNN) I/Q imbalance dc offset power amplifier (PA) |
| title | Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments |
| title_full | Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments |
| title_fullStr | Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments |
| title_full_unstemmed | Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments |
| title_short | Complex-Valued Pipelined Chebyshev Functional Link Recurrent Neural Network for Joint Compensation of Wideband Transmitter Distortions and Impairments |
| title_sort | complex valued pipelined chebyshev functional link recurrent neural network for joint compensation of wideband transmitter distortions and impairments |
| topic | Digital predistortion (DPD) complex-valued pipelined Chebyshev functional link recurrent neural network (CPCFLRNN) I/Q imbalance dc offset power amplifier (PA) |
| url | https://ieeexplore.ieee.org/document/9184015/ |
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