This study proposes a new biased extended memory polynomial model and a new clustering enhanced recursive least squares algorithm, which jointly innovate the digital pre-distortion algorithm. Compared with the conventional digital pre-distortion algorithm, it has higher accuracy without increasing complexity. The proposed biased extended memory polynomial model introduces a constant term to explain the DC offset and system error, and shows special effectiveness under low amplitude excitation. At the same time, the clustering enhanced recursive least squares algorithm uses K-means clustering to partition the nonlinear feature space, thereby realizing coefficient propagation between clusters, realizing local parameter updates, and significantly accelerating the convergence speed, improving convergence stability and accuracy.
KimMLeeWChoD -H. A novel PAPR reduction scheme for OFDM system based on deep learning. IEEE Commun Lett2018; 22(3): 510–513.
2.
CheangC -FMakP -IMartinsRP. A hardware-efficient feedback polynomial topology for DPD linearization of power amplifiers: theory and FPGA validation. IEEE Trans Circuits Syst I2018; 65(9): 2889–2902.
3.
HuXLiuZYuX, et al.Convolutional neural network for behavioral modeling and predistortion of wideband power amplifiers. IEEE Trans Neural Netw Learn Syst2022; 33(8): 3923–3937.
4.
LiuYPanWShaoS, et al.A general digital predistortion architecture using constrained feedback bandwidth for Wideband power amplifiers. IEEE Trans Microw Theory Tech2015; 63(5): 1544–1555.
5.
RenJ. A new digital predistortion algorithms scheme of feedback FIR cross-term memory polynomial model for short-wave power amplifier. IEEE Access2020; 8: 38327–38332.
6.
ZhuTJPedroJC. Brazil, “dynamic deviation reduction-based volterra behavioral modeling of RF power amplifiers”. IEEE Trans Microw Theory Tech2006; 54(12): 4323–4332.
7.
KimJKonstantinouK. Digital predistortion of wideband signals based on power amplifier model with memory. Electron Lett2002; 37(23): 1417–1418.
8.
DingLZhouGMorganD, et al.A robust digital baseband predistorter constructed using memory polynomials. IEEE Trans Commun2004; 52(1): 159–165.
9.
MaZDingYZhuD, et al.A generalized memory polynomial model for digital predistortion of RF power amplifiers. IEEE Trans Signal Process2012; 60(6): 3272–3283.
10.
WangXLiYZhuA. Digital predistortion using extended Magnitude- selective affine functions for 5G handset power amplifiers with load mismatch. IEEE Trans Microw Theory Tech2022; 70(5): 2825–2834.
11.
SmirnovV. Optimization of digital predistortion with memory. In: 2018Systems of signal synchronization, generating and processing in telecommunications (SYNCHROINFO). Minsk, Belarus, 2018, pp. 1–6. DOI: 10.1109/SYNCHROINFO.2018.8456943.
12.
PrestiCDKimballDFAsbeckPM. Closed-loop digital predistortion system with fast real-time adaptation applied to a handset WCDMA PA module. IEEE Trans Microw Theory Tech2012; 60(3): 604–618.
13.
GilabertPLBertranEMontoroG, et al.FPGA implementation of an LMS-Based real-time adaptive predistorter for power amplifiers. In: 2009 joint IEEE North-East workshop on circuits and systems and TAISA conference. Toulouse, France, 2009, pp. 1–4. DOI: 10.1109/NEWCAS.2009.5290454.
14.
PagadaraiSSpaniasASorensenLD. Digital predistortion of power amplifiers for spectrally agile wireless transmitters. IEEE Trans Commun2016; 60(6): 1587–1592.
15.
HuangTHongWCaiT, et al.RLS-DPD algorithm for hybrid precoding architecture in MIMO-OFDM systems. In: 2020 IEEE 3rd international conference on electronic information and communication technology (ICEICT), Shenzhen, China, 2020, pp. 20–24. DOI: 10.1109/ICEICT51264.2020.9334360.
16.
YinHYuZYuC, et al.Data-clustering-assisted digital predistortion for 5G millimeter-wave beamforming transmitters with multiple dynamic configurations. IEEE Trans Microw Theory Tech2021; 69(3): 1805–1816.
17.
FanDLvCCaiQ. SVD filter based on noise singular values clustering. In: 2010 international conference on intelligent computation technology and automation, Changsha, China, 2010, pp. 665–668. DOI: 10.1109/ICICTA.2010.835.
18.
ChuaLODengA-C. Canonical piecewise-linear representation. IEEE Trans Circuits Syst1988; 35(1): 101–111.
19.
GuYTangKCuiH. LMS algorithm with gradient descent filter length. IEEE Signal Process Lett2004; 11(3): 305–307.
20.
LahmarAZYahiaMBouallegueR. A new self adaptive fuzzy unsupervised clustering ensemble based on spectral clustering. In: 2020 17th international multi-conference on systems, signals & Devices (SSD), Monastir, Tunisia, 2020, pp. 1–5. DOI: 10.1109/SSD49366.2020.9364223.
21.
MinminWShengliSYejinL. A compressive tracking method based on Gaussian differential graph and weighted cosine similarity metric. IEEE Signal Process Lett2018; 25(4): 501–505.
22.
LiKXiaoJXieJ, et al.A novel adaptive variable forgetting factor RLS algorithm. In: 2022 international conference on informatics, networking and computing (ICINC), Nanjing, China, 2022, pp. 228–232. DOI: 10.1109/ICINC58035.2022.00053.
