针对宽带功率放大器设计的人工神经网络算法

doi:10.19665/j.issn1001-2400.2019.06.017

Abstract

Abstract:

The artificial neural network modeling approach to designing matching networks for the broadband high efficiency power amplifiers (PAs) is proposed for the first time. The effects of input and output matching networks on the performance of PAs are analyzed. The neural network model is exploited to represent the relationship between matching networks and the optimal impedances, and the design process is combined with the optimization method. The developed ANN model allows the RF amplifier designers to realize specified matching networks conveniently and effectively. Circuit examples are used to demonstrate our proposed method. Simulation results show that a broadband high-efficiency PA is realized from 0.2 to 1.6 GHz (fractional bandwidth = 156%) with the simulated drain efficiency of 64.5%~80.5% and the output power of 40.4~41.6 dBm (10~14.5W).

Key words: power amplifiers, neural network, impedance matching, broadband networks, drain efficiency

CLC Number:

TN41

ZHANG Mingzhe,WU Haifeng,WEI Shizhe. Design of a broadband high-efficiency power amplifier using the artificial neural network[J].Journal of Xidian University, 2019, 46(6): 118-124.

Figures/Tables 13

References 18

[1]	MENG X, YU C, LIU Y , et al. Implementation of Flat Gain Broadband Power Amplifier with Impedance Rotation Compensation[J]. IEEE Access, 2019,7(1):13304-13316. doi: 10.1109/Access.6287639
[2]	张瑛, 马凯学, 张翼 , 等. 2.5~14.5GHz分布式功率放大器设计[J] . 西安电子科技大学学报, 2018,45(1):88-92.
	ZHANG Ying, MA Kaixue, ZHANG Yi , et al. Design of a 2.5~14.5GHz Distributed Power Amplifier[J]. Journal of Xidian University, 2018,45(1):88-92.
[3]	LIU C, CHENG Q F . Analysis and Design of High-efficiency Parallel-circuit Class-E/F Power Amplifier[J]. IEEE Transactions on Microwave Theory and Techniques, 2019,67(6):2382-2392. doi: 10.1109/TMTT.22
[4]	YANG M, XIA J, GUO Y , et al. Highly Efficient Broadband Continuous Inverse Class-F Power Amplifier Design Using Modified Elliptic Low-pass Filtering Matching Network[J]. IEEE Transactions on Microwave Theory and Techniques, 2016,64(5):1515-1525. doi: 10.1109/TMTT.2016.2544318
[5]	TUFFY N, GUAN L, ZHU A D , et al. A Simplified Broadband Design Methodology for Linearized High-efficiency Continuous Class-F Power Amplifiers[J]. IEEE Transactions on Microwave Theory and Techniques, 2012,60(6):1952-1963. doi: 10.1109/TMTT.2012.2187534
[6]	MENG F, ZHU X W, XIA J , et al. A New Approach to Design a Broadband Doherty Power Amplifier via Dual-transformation Real Frequency Technique[J]. IEEE Access, 2018,6(9):48588-48599. doi: 10.1109/ACCESS.2018.2868238
[7]	王乐, 赵志鹏, 李亚楠 , 等. 多阶阻抗匹配与宽频带天线的设计分析[J]. 西安电子科技大学学报, 2018,45(5):75-80.
	WANG Le, ZHAO Zhipeng, LI Yanan , et al. Design and Analysis of Multi-tuned Impedance Matching and Broadband Antennas[J]. Journal of Xidian University, 2018,45(5):75-80.
[8]	ZHUANG Y, FEI Z X, CHEN A Q , et al. Design of Multioctave High-efficiency Power Amplifiers Using Stochastic Reduced Order Models[J]. IEEE Transactions on Microwave Theory and Techniques, 2018,66(2):1015-1023. doi: 10.1109/TMTT.2017.2750164
[9]	YARMAN B S, CARLIN H J . Simplified “Real Frequency” Technique Applied toBroad-band Multistage Microwave Amplifiers[J]. IEEE Transactions on Microwave Theory and Techniques, 1982,30(12):2216-2222. doi: 10.1109/TMTT.1982.1131411
[10]	CHEN P, MERRICK B M, BRAZIL T J . Bayesian Optimization for Broadband High-efficiency Power Amplifier Designs[J]. IEEE Transactions on Microwave Theory and Techniques, 2015,63(12):4263-4272. doi: 10.1109/TMTT.2015.2495360
[11]	ZHANG C, JIN J, NA W C , et al. Multivalued Neural Network Inverse Modeling and Applications to Microwave Filters[J]. IEEE Transactions on Microwave Theory and Techniques, 2018,66(8):3781-3797. doi: 10.1109/TMTT.2018.2841889
[12]	ZHANG Q J, GUPTA K C, DEVABHAKTUNI V K . Artificial Neural Networks for RF and Microwave Design-from Theory to Practice[J]. IEEE Transactions on Microwave Theory and Techniques, 2003,51(4):1339-1350. doi: 10.1109/TMTT.2003.809179
[13]	FENG F , GONGAL-REDDY V M R, ZHANG C, et al. Parametric Modeling of Microwave Components Using Adjoint Neural Networks and Pole-residue Transfer Functions with EM Sensitivity Analysis[J]. IEEE Transactions on Microwave Theory and Techniques, 2017,65(6):1955-1975. doi: 10.1109/TMTT.2017.2650904
[14]	ZHU L, ZHANG Q, LIU K , et al. A Novel Dynamic Neuro-space Mapping Approach for Nonlinear Microwave Device Modeling[J]. IEEE Microwave and Wireless Components Letters, 2016,26(2):131-133. doi: 10.1109/LMWC.2016.2516761
[15]	陈昀, 蔡晓东, 梁晓曦 , 等. 权重量化的深度神经网络模型压缩算法[J]. 西安电子科技大学学报, 2019,46(2):132-138.
	CHEN Yun, CAI Xiaodong, LIANG Xiaoxi , et al. Compression Algorithm for Weights Quantized Deep Neural Network Models[J]. Journal of Xidian University, 2019,46(2):132-138.
[16]	MENG X, YU C, LIU Y , et al. Design Approach for Implementation of Class-J Broadband Power Amplifiers Using Synthesized Band-pass and Low-pass Matching Topology[J]. IEEE Transactions on Microwave Theory and Techniques, 2017,65(12):4984-4996.
[17]	JIA P, YOU F, HE S , et al. A 0.25-1.25-GHz High-efficiency Power Amplifier with Computer-aided Design Based on Optimized Impedance Solution Continuum[J]. IEEE Microwave and Wireless Components Letters, 2018,28(5):443-445. doi: 10.1109/LMWC.2018.2814483
[18]	VAI M, PRASAD S . Automatic Impedance Matching with a Neural Network[J]. IEEE Microwave and Guided Wave Letters, 1993,3(10):353-354. doi: 10.1109/75.242258

变量	训练数据			测试数据
变量	最小值	最大值	数目/个	最小值	最大值	数目/个
L₁/nH	0.2	6	10 000	0.5	5.5	1 000
L₂/nH	0.2	6	10 000	0.5	5.5	1 000
L₃/nH	0.2	6	10 000	0.5	4.5	1 000
C₁/pF	0.2	5	10 000	0.5	4.5	1 000
C₂/pF	0.2	5	10 000	0.5	4.5	1 000
C₃/pF	0.2	5	10 000	0.5	4.5	1 000

输入阻抗(Z_in)	神经元数/个	训练误差/%	测试误差/%
Z_in@0.3GHz	14	0.03	0.04
Z_in@0.5GHz	14	0.13	0.13
Z_in@0.7GHz	20	0.17	0.16
Z_in@1.0GHz	26	0.36	0.35
Z_in@1.2GHz	32	0.51	0.51

序号	神经网络初值						反向优化后结果
序号	L₁/nH	C₁/pF	L₂/nH	C₂/pF	L₃/nH	C₃/pF	L₁/nH	C₁/pF	L₂/nH	C₂/pF	L₃/nH	C₃/pF
1	5.79	0.67	2.13	1.55	1.94	2.79	4.81	0.86	1.05	1.13	1.21	1.32
2	0.53	3.66	4.60	2.15	4.91	2.23	5.03	0.85	1.05	1.94	1.98	0.85
3	0.96	1.68	2.53	1.29	3.19	3.62	4.25	0.85	1.38	0.89	1.56	1.69
4	2.70	3.65	3.94	1.10	3.68	1.67	5.42	3.52	3.77	1.06	3.82	0.99
5	4.90	0.89	0.95	2.02	3.02	1.19	4.72	0.89	1.05	1.94	3.16	1.06

Design of a broadband high-efficiency power amplifier using the artificial neural network

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 18

Related Articles 15

Metrics

Comments

Recommended 10

[1]	LV Wenkai,YANG Pengfei,DING Yunqing,ZHANG Heyu,ZHENG Tianyang. JEDERL:A task scheduling optimization algorithm for heterogeneous computing platforms [J]. Journal of Xidian University, 2021, 48(6): 67-74.
[2]	YU Haoyang,YIN Liang,LI Shufang,LV Shun. Recognition algorithm for the little sample radar modulation signal based on the generative adversarial network [J]. Journal of Xidian University, 2021, 48(6): 96-104.
[3]	HU Daiwang,JIAO Yiyuan,LI Yanni. Novel and efficient algorithm for entity relation extraction with the corpus knowledge graph [J]. Journal of Xidian University, 2021, 48(6): 75-83.
[4]	SUN Yanjing,WEI Li,ZHANG Nianlong,YUN Xiao,DONG Kaiwen,GE Min,CHENG Xiaozhou,HOU Xiaofeng. Person re-identification method combining the DD-GAN and Global feature in a coal mine [J]. Journal of Xidian University, 2021, 48(5): 201-211.
[5]	ZHOU Peng,YANG Jun. Semantic segmentation of remote sensing images based on neural architecture search [J]. Journal of Xidian University, 2021, 48(5): 47-57.
[6]	ZHANG Shuwei,LI Junmin. Human body detection algorithm in complex monitoring scenes [J]. Journal of Xidian University, 2021, 48(5): 68-77.
[7]	YANG Yunhang,MIN Lianquan. Multi-scalefusion sketch recognition model by dilated convolution [J]. Journal of Xidian University, 2021, 48(5): 92-99.
[8]	DONG Ruchan,JIAO Licheng,ZHAO Jin,SHEN Weiyan. Application of the deep fusion mechanism in object detection of remote sensing images [J]. Journal of Xidian University, 2021, 48(5): 128-138.
[9]	CHENG De,HAO Yi,ZHOU Jingyu,WANG Nannan,GAO Xinbo. Cross-modality person re-identification utilizing the hybrid two-stream neural networks [J]. Journal of Xidian University, 2021, 48(5): 190-200.
[10]	CHEN Changchuan,WANG Haining,HUANG Lian,HUANG Tao,LI Lianjie,HUANG Xiangkang,DAI Shaosheng. Facial expression recognition based on local representation [J]. Journal of Xidian University, 2021, 48(5): 100-109.
[11]	SONG Jianfeng,MIAO Qiguang,WANG Chongxiao,XU Hao,YANG Jin. Multi-scale single object tracking based on the attention mechanism [J]. Journal of Xidian University, 2021, 48(5): 110-116.
[12]	ZHANG Yuhao,CHENG Peitao,ZHANG Shuhao,WANG Xiumei. Lightweight image super-resolution with the adaptive weight learning network [J]. Journal of Xidian University, 2021, 48(5): 15-22.
[13]	HAN Yongsai,MA Shiping,HE Linyuan,LI Chenghao,ZHU Mingming,ZHANG Fei. Detection of the object in the fast remote sensing airport area on the improved YOLOv3 [J]. Journal of Xidian University, 2021, 48(5): 156-166.
[14]	HUI Haisheng,ZHANG Xueying,WU Zelin,LI Fenglian. Method for stroke lesion segmentation using the primary-auxiliary path attention compensation network [J]. Journal of Xidian University, 2021, 48(4): 200-208.
[15]	SONG Jianqiao,WANG Feng,NIU Jin,SHI Zezhou,MA Junhui. Potential emotion recognition based on the fusion of the spatio-temporal neural network and facial pulse signals [J]. Journal of Xidian University, 2021, 48(4): 159-167.