| Recent Progresses in Digital Predistortion Based on Machine Learning |
| Click here to download the full text |
| Citation of this paper: |
| Hits: 4064 |
| Download times: 948 |
| Author Name | Affiliation | | LIU Fa-lin1,2,ZHANG Qian-qian1,2,WANG Jun-sen1,2,CHANG Hao1,2,JIANG Cheng-ye1,2,YANG Gui-chen1,2,HAN Ren-long1,2 | 1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China
2. Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei 230027, China |
|
| Fund Project:国家自然科学基金(62371436) |
| |
| Abstract:Digital predistortion (DPD) techniques are now widely used to correct the nonlinearity of power amplifiers (PAs) and reduce power dissipation in the transmitter front-ends. With the development of communication technology, high-performance and low-complexity DPD technology has become a hot spot of current research. The development of machine learning (ML) provides new ideas for research and plays an important role in the development process of DPD. Based on ML, this paper focuses on the three research directions of model construction, parameter extraction and varying transmission configurations DPD, summarises the relevant literature, and elaborates the existing methods in each direction. |
| keywords:digital predistortion (DPD), machine learning (ML), power amplifier (PA), parameter extraction,varying transmission configurations |
| HTML View Full Text View/Add Comment Download reader |
|
|
|
