A Digital Predistortion Technique Based on the Dimension Weighted Blind K-Nearest Neighbor Algorithm

JIANG Weiheng; DUAN Yaoxing; LI Mingyu; JIN Yi; XU Changzhi; LI Li

doi:10.11999/JEIT220302

Volume 45 Issue 2

Feb. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(2): 446-454

JIANG Weiheng, DUAN Yaoxing, LI Mingyu, JIN Yi, XU Changzhi, LI Li. A Digital Predistortion Technique Based on the Dimension Weighted Blind K-Nearest Neighbor Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(2): 446-454. doi: 10.11999/JEIT220302

Citation:

JIANG Weiheng, DUAN Yaoxing, LI Mingyu, JIN Yi, XU Changzhi, LI Li. A Digital Predistortion Technique Based on the Dimension Weighted Blind K-Nearest Neighbor Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(2): 446-454. doi: 10.11999/JEIT220302

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A Digital Predistortion Technique Based on the Dimension Weighted Blind K-Nearest Neighbor Algorithm

doi: 10.11999/JEIT220302

1.
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
2.
China Academy of Space Technology-Xi’an, Xi’an 710100, China

Funds: The National Natural Science Foundation of China (62171068, 62001061), The QingNian Project of Science and Technology Research Program of Chongqing Education Commission of China (KJQN201902403)

Received Date: 2022-03-21
Rev Recd Date: 2022-09-19

Available Online: 2022-09-27

Publish Date: 2023-02-07

Abstract

Abstract

In traditional Digital PreDistortion (DPD) models, the same set of polynomial models and the same memory model are usually used to linearize the Power Amplifier (PA) at all input signal powers. However, the PAs exhibit different nonlinear characteristics and different memory effects at different power levels. In order to solve this problem, a DPD model based on the blind K-Nearest Neighbor (KNN) algorithm with dimension weighting is proposed. The input signal sequence is classified by the proposed model according to the magnitudes of amplifier's current input signal and the memory input signal with the dimension-weighted KNN classification .And sub-models are established for each type of input signal sequence. The proposed method is verified experimentally by a Doherty PA, a three carrier Long Term Evolution (LTE) signal with a bandwidth of 30 MHz and a frequency point of 2.2 GHz is used as the input, the feedback channel is sampled using a sampling rate of 122.88 MHz. When the dimensional-weighted blind KNN classification method is combined with the Memory Polynomial (MP) model, the forward modeling performance and digital pre-distortion performance for the PA which exceed the performance of Generalized Memory Polynomial (GMP) model and MP model are manifested in the experiment. The excellent performance of the proposed model is verified in the experiment.
- Digital PreDistortion(DPD),
- Blind K-Nearest Neighbor(KNN) classification,
- Dimension weighting,
- Power Amplifier(PA),
- Behavioral model

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References(17)

References

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