The Journal of China Universities of Posts and Telecommunications February 2016, 23(1): 91–96 www.sciencedirect.com/science/journal/10058885
http://jcupt.bupt.edu.cn
Design of High-Efficiency broadband power amplifier using low-pass bias network Yu Qijin (
), Yu Cuiping, Tang Bihua, Liu Yuan’an
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Abstract A novel design of high-efficiency broadband power amplifier (BPA) with the low-pass bias networkto enhance the efficiency and output power is presented in this paper. Compared with other bias networks, the proposed low-pass bias network shows a smaller baseband impedance, which can reduce the electrical memory effect. While it provides a larger radio frequency (RF) impedance, which can prevent the leakage of the output power from bias network. A BPA with the proposed bias network is designed using commercial GaN device Cree40025F. The designed BPA shows a fractional bandwidth of 40%, from 1.8 GHz to 2.7 GHz. The measured results exhibit 73.9 % drain efficiency (DE) value with output power of 43.5 dBm at 2.7 GHz, which appears an enhancement of 9.5% and 2.5 dBm comparing with that adopts LC bias network. Keywords broadband power amplifier, low-pass bias network, high-efficiency, broadband
1 Introduction Modern wireless communication systems achieve high data throughput and network capacity on RF signal transmission utilizing an ever-increased number of frequency bands. This requires a high-efficiency power amplifier (PA) working in a wide frequency band. Hence, people show more and more interest in BPA. As the signal bandwidth increases, the impact of electrical memory effects on the linearity of PAs cannot be ignored. To minimize the memory effects, the baseband impedance should be reduced as much as possible [1–2]. To address this problem, the LC series resonant bias network of BPA was proposed in Refs. [3–4]. Unfortunately, the impedances of the fundamental frequency which are supposed to be large enough to prevent power leakage from bias network were also reduced in above researches. A bias circuit with envelope frequency decoupling capacitors located after the series RF inductance was used in Ref. [5], which enlarges the impedance of the fundamental frequency, at the cost of Received date: 09-10-2015 Corresponding author: Yu Qijin, E-mail:
[email protected] DOI: 10.1016/S1005-8885(16)60013-4
high baseband impedance. Obviously, bias network, as an important part of BPA, has to be given more attention, as described in previous literatures. In order to achieve low baseband impedance and high impedance of the fundamental frequency simultaneously, a low-pass filter (LPF) as the bias network is implemented with microstrip line in this paper.
2 Design of the broadband highly efficient PA 2.1
Proposed low-pass filter bias network.
According to the basic filter theory, the perfect lowpass filter would have zero insertion loss with a frequency lower than a certain cutoff frequency and infinite attenuation in the stop band. A LPF in Ref. [6] whose cutoff frequency f n is less than the operating frequency of the PA is applied to achieve the required bias network as presented in Fig. 1(c).Three different kinds of bias network (resonator-type bias network, conventional inductance series bias network, and the proposed low-pass bias network) as shown in Fig. 1 are simulated in the advanced design system (ADS) 2011.
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obtained at 100 MHz and 200 MHz, respectively, as presented in Fig. 2.
(a) LC resonant circuit with decoupling capacitors
(a) LC resonant bias network (b) Series RF inductance bias network (c) Low-pass bias network Fig. 2 Baseband impedance of bias networks (b) Series RF inductance with decoupling capacitors
The small RF impedance of the bias networks will cause power leakage. The general PA output circuit can be converted to the equivalent model as shown in Fig. 3. Pout is the power dissipated in the load,
1 Re [Vload I load ] (1) 2 We know that maximum power transferred from the output matching network to the load will occur when Z load is conjugately matched to generator impedance Z G . Z G Pout =
is the impedance looked toward the output plane of * intrinsic device. Vload and I load can be expressed as
(c) LPF with decoupling capacitors Fig. 1 Schematic diagrams of bias networks
Comparing bias networks (a), (b) and (c), smaller baseband impedance can be acquired by the low-pass bias network, the load impedance of 4.8 Ω and 15.5 Ω are
Vload = VG
Z load / / Z bias Z G + Z load / / Z bias
(2)
(a) General PA output circuit (b) Equivalent model Fig. 3 Equivalent circuit of PA *
I
* load
=
VG* ( Z load / / Zbias )
1 *
( Z G + Z load / / Z bias )
* Z load
2
(3)
Where Z bias is the impedance seen into bias network.
Pout can be deduced by eliminating variables into
Z load / / Zbias 1 1 Pout = VG2 Re Z G + Z load / / Z bias 2 Z load As we know
(4)
Issue 1
1 Re Z load
Yu Qijin, et al. / Design of High-Efficiency broadband power amplifier using low-pass bias network
Re ( Z load ) = 2 Z load
(5)
Mathematically, the formula can be rewritten as Z 1 2 Pout = VG2 Re ( Z load ) 4 Re ( Z load ) + 2 Re ( Z load ) bias 2 ⋅ 2 Z bias −1
2 Z load + (6) 2 Z bias From the formula above, it can be obtained that the output power of the PA will be increased when the resistance and the reactance of Z bias are increased. The peak power can be calculated when Z bias is increased to
Z load
4
infinity as follow 1 1 Pout,peak = VG2 8 Re ( Z load )
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bias network. Therefore smaller power leakage rate can be obtained by calculation. At 2.7 GHz, the power leakage rate of low-pass bias network is 6% and 15% smaller than the other bias networks, leading to higher efficiency and larger output power. 2.2
Design of broadband power amplifier
The complete scheme of the proposed BPA, as presented in Fig. 5(a), contains three parts primarily: broadband input matching network, broadband output matching network, and low-pass bias network. Furthermore, the conventional BPA with the same structure except the LC bias network as the proposed BPA is presented in Fig. 5(b).
(7)
From Eq. (6) and Eq. (7), we establish P 2 2 µ = 1 − out = 1 − Re ( Z load ) 4 Re ( Z load ) + Pout,peak −1
2 Re ( Z load ) Z + (8) load 2 2 Z bias Z bias Where power leakage rate µ is the ratio of the power Z bias
2
Z load
4
(a) Low-pass BPA
leakage to the bias tee to the maximum power available from the output network. We will obtain the optimum impedance at different frequencies by load-pull simulation or measurement. After that µ can be calculated and plotted in Fig. 4.
Fig. 5
(a) LC resonant bias network (b) Series RF inductance bias network (c) Low-pass bias network. Fig. 4 Load impedance and power leakage of the bias networks at RF frequency
As presented in Fig. 4, higher impedances of the fundamental frequency can be received by the low-pass
(b) LC bias BPA Top view of the fabricated PAs
The output matching network is reported in Fig.6, which is designed by applying high order low-pass matching networks. In order to transform the optimal load impedances to 50 Ω in a wide range, the output matching network is designed. Firstly, the optimum load impedances at desired frequencies are obtained by load-pull simulation or measurement. After that, according tothe method mentioned in Ref. [7], a real-to-real LC low-pass matching network with 5:1 transformation ratio and 60% bandwidth can be designed by using three-stage Chebyshev impedance transformer at a center frequency of 2.2 GHz.
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And then a desired real-to-complex LC low-pass prototype can be obtained by enlarge L1 from 0.72 nH to 1.42 nH as shown in Fig. 6(a).
2016
The LC low-pass prototype can be implemented by transmission lines, whose physical lengths have been optimized, as presented in Fig. 6(a). Achieved fundamental and harmonic load impedance of the proposed PA are shown in Fig. 6(b).
3 Implementation and experimental results The simulated and measured scattering parameters of the BPA with low-pass bias network are shown in Fig. 7(a). In the working band, the S11 is below − 14.5 dB, the
S21 is greater than 12.5 dB from 1.8 GHz to 2.7 GHz with
(a) High order LC low-pass prototype matching network
(b) Achieved fundamental and harmonic load impedance Fig. 6 Output matching network of proposed PA
Given the uncertainty of inductors and capacitors at the desired frequency range, the low-pass matching network is realized with microstrip line. According to Ref. [8], the inductors are replaced by high-impedance (high-Z) transmission-line sections and the capacitors are replaced by low-impedance (low-Z) open-circuit stubs. The transmission lines are calculated by ωL ω L ≈ ZL β l ⇒ l ≈ (9) β ZL
1
ωC
=2
ZC arctan(2ω Z C C ) ⇒l = tan( β l ) β
a peak of 14.6 dB. Obviously, the measured scattering parameters of the BPA with low-pass bias network are feasible in the working band. The BPA was fabricated on a 20 mil thick Rogers RO4350B substrate with a 3.48 dielectric constant. The GaN HEMT Cree40025F with 25 W saturation power was used for the broadband amplifier. The gate bias voltage of the amplifier was set to a class-AB bias of − 2.86 V (quiescent current, IDQ=200 mA) with a drain bias voltage of 28 V. The measured output spectrums with and without digital predistortion (DPD) are shown in Fig. 7(b), 7(c). At the average output power of 35.5 dBm for the 20 MHz modulated signal centered at 2.65 GHz, the adjacent channel power ratio (ACPR) of BPA with conventional bias network could be corrected to − 45 dBc after DPD, while it could be linearized to − 51 dBc with low-pass bias network. To measure the power added efficiency (PAE), gain, and output power of the fabricated BPA, continuous wave (CW) signals were applied. Fig. 7(c) shows that the proposed BPA provides 69.5% to 75.9% DE and 42.5 dBm to 45dBm power at saturation from 1.8 GHz to 2.7 GHz.
(10)
The characteristic impedances of the high-Z and low-Z impedance are chosen as Z L = 110 Ω and Z C = 40 Ω .
(a) Simulated and measured scattering parameters of the low-pass BPA
Issue 1
Yu Qijin, et al. / Design of High-Efficiency broadband power amplifier using low-pass bias network
(b) Measured spectrum at an average output power of 35.5 dBm at 2.65 GHz of LC bias BPA
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low-pass bias network has been presented based on the analysis of the influence on the impedance of bias network. Compared with recently published PAs with various bias networks, the BPA with proposed bias network achieves better linearity, as shown in Table 1. Compared with Ref. [3], the proposed PA achieves larger fractional bandwidth. Compared with Ref. [5] and Ref. [8], the proposed PA achieves higher efficiency in the edge of working band. The BPA with the proposed bias network exhibits higher efficiency and better linearity performance than the BPA with conventional bias networks in the edge of the working band. Table 1
Comparison of high-efficiency broadband PAs
Ref. or work
Band width /(GHz,%)
Pout/dBm
DE/%
Ref. [3] Ref. [5] Ref. [8] This work (LC bias) This work (Low-pass bias)
0.698~0.862,21 2.0~3.5,55 0.9~2.2,84
49.5 40.5~42.0 40.0~43.0
75~80 64~76 63~89
ACPR with DPD /dBc − 48 − 48 − 40
1.8~2.7,40
41.0~44.5 64.4~74.3
− 45
1.8~2.7,40
43.5~45.0 69.5~75.9
− 51
Acknowledgements This work was supported by National Basic Research Program of China (973 Program) (2014CB339900), National Natural Science (c) Measured spectrum at an average outputpower of 35.5 dBm at 2.65 GHz of LC bias BPA on the right and low-pass BPA on the left
Foundation of China (61201025), and National Natural Science Foundation of China for the Major Equipment Development (61327806).
References
(d) CW measured results of the low-pass bias BPA and LC bias BPA Fig. 7 Measurements of the proposed PA
As presented in Fig. 7(d). It shows that higher efficiency and larger output power can be obtained by the low-pass bias network.
4 Conclusions In this paper, a broadband highly efficient GaN PA with
1. Franco M, Guido A, Katz A, et al. Minimization of bias-induced memory effects in UHF radio frequency high power amplifiers with broadband signals. Proceedings of the 2007 IEEE Radio Wireless Symposium, Jan 9−11, 2007, Long Beach, CA, USA. Piscataway, NJ, USA: IEEE, 2007: 367−372 2. Brinkhoff J, Parker A E, Leung M. Baseband impedance and linearization of FET circuits. IEEE Transactions on Microwave Theory and Techniques, 2003, 51(12): 2523−2530 3. Ma C H, Pan W S. Shao S H, et al. A wideband Doherty power amplifier with 100 MHz instantaneous bandwidth for LTE-advanced applications. IEEE Microwave and Wireless Components Letters, 2013, 23 (11): 614−616 4. Takenaka I, Ishikura K, Takahashi H, et al. Improvement of intermodulation distortion asymmetry characteristics with wideband microwave signals in high power amplifiers. IEEE Transactions on Microwave Theory and Techniques, 2008, 56 (6): 1355−1363 5. Xia J, Zhu X W, Zhang L. A linearized 2−3.5 GHz highly efficient harmonic-tuned power amplifier exploiting stepped-impedance filtering matching network. IEEE Microwave and Wireless Components Letters, 2014, 24(9): 602−604 6. Wu Y, Liu Y, Li S, et al. A new wide-stopband low-pass filter with generalized coupled-line circuit and analytical theory. Progress in Electromagnetics Research, 2011, 116: 553−567 7. Matthaei G L. Table of Chebyshev impedance-transformation networks of
96
The Journal of China Universities of Posts and Telecommunications
low-pass filter form. Proceedings of the IEEE, 1964, 52(8): 939−963 8. Chen K L, Peroulis D. Design of highly efficient broadband class-E power
2016
amplifier using synthesized low-pass matching networks. IEEE Transactions on Microwave Theory and Techniques, 2011, 59(12): 3162−3173
(Editor: Lu Junqiang)
From p. 72 4. Hou X M. A SVM multi classification algorithm for anti-noise speech recognition. Journal of Xi’an University of Posts and Telecommunications, 2009, 14(5):100−105 (in Chinese) 5. Lee Y J, Mangasarian O L. SSVM: A smooth support vector machine for classification. Computational Optimization and Applications, 2001, 20(1): 5−22 6. Yuan Y B, Huang T Z. A polynomial smooth support vector machine for classification. Advanced Data Mining and Applications: Proceedings of the 1st International Conference on Advanced Data Mining and Applications (ADMA’05), Jul 22−24, 2005, Wuhan, China. LNCS 3584. Berlin, Germany: Springer, 2005: 157−164 7. Yuan Y B, Fan W G, Pu D M. Spline function smooth support vector machine for classification. Journal of Industrial and Management Optimization, 2007, 3(3): 529−542
8. Yuan B L, Zhang W J, Wu H. New solution method to smoothing support vector machine with one control parameter smoothing function. Proceedings of the 2nd WRI Global Congress on Intelligent Systems (GCIS’10): Vol 2, Dec 16−17, 2010, Wuhan, China. Piscataway, NJ, USA: IEEE, 2010: 153−156 9. Wu Q, Fan J L. Smooth support vector machine based on piecewise function. The Journal of China Universities of Posts and Telecommunications, 2013, 20(5): 122−128 10. Wu Q, Wang W, Wang L Z. Exponential smooth support vector machine. Journal of Xi’an University of Posts and Telecommunication, 2014, 19(4): 9−14 (in Chinese) 11. Yuan Y K, Byrd R H. Non-quasi-Nowton updates for unconstrained optimization. Journal of Computing Mathematics, 1995, 13(2): 95−107 12. Yuan Y X. A modified BFGS algorithm for unconstrained optimization. IMA Journal of Numerical Analysis, 1991, 11(3): 325−332
(Editor: Lu Junqiang)
From p. 85 20. Yilmaz A, Javed O, Shah M. Object tracking: A survey. ACM Computing Surverys, 2006, 38(4): 1−45 21. Park Y, Lepetit V, Woo W. Extended keyframe detection with stable tracking for multiple 3D object tracking. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(11): 1728−1735 22. de Haan G. Progress in motion estimation for consumer video format conversion. IEEE Transactions on Consumer Electronics, 2000, 46(3): 449−459 23. Chien S Y, Ma S Y, Chen L G. Efficient moving object segmentation algorithm using background registration technique. IEEE Transactions on Circuits and Systems for Video Technology, 2002, 12(7): 577−585 24. Tsalatsanis A, Valavanis K, Yalcin A. Vision based target tracking and collision avoidance for mobile robots. Journal of Intelligent and Robotic Systems, 2007, 48(2): 285−304
25. Gao X, Hu H, Jia Q X, et al. 3-D augmented reality teleoperated robot system based on dual vision. The Journal of China Universties of Posts and Telecommunications, 2011, 18(1): 105−112 26. Jia Q X, Zhang Q R, Gao X, et al. Dynamic obstacle avoidance algorithm for redundant robot with pre-selected minimum distance index. Robot, 2013, 35(1): 17−22 (in Chinese) 27. Fragkopoulos C, Graser A. Dynamic efficient collision checking method of robot arm paths in configuration space. Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM’11), Jul 3−7, 2011, Budapest, Hungary. Piscataway, NJ, USA: IEEE, 2011: 784−789 28. Gao X, Jia Q X, Sun H X, et al. A multi-layer occlusion handling method based on stereo vision and pose estimation for real-time augmented reality. Journal of Information and Computational Science, 2010, 7 (12): 2548−2555
(Editor: Lu Junqiang)