Light-Weight AI Enabled Non-Linearity Compensation Leveraging High Order Modulations

Bin Yu,Chen Qian, Peng Lin,Juho Lee, Qi Li, Seungil Park, Suhwook Kim, Changbae Yoon,Su Hu,Lingjia Liu

IEEE TRANSACTIONS ON COMMUNICATIONS(2024)

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摘要
The non-linear distortion caused by non-ideal radio frequency (RF) components especially the power amplifier (PA) limits the applications of higher order modulation and degrades power utilization efficiency. To improve the achievable rate in modern systems, it becomes critical to overcome the non-linear distortion so that we can maximize the opportunity of using higher order modulation such as 256QAM, 1024QAM and even 4096QAM at high transmission power. In this paper, we introduce an artificial intelligence (AI)-enabled non-linearity compensation scheme (AI-NC) to avoid the "model deficit" problem. The introduced AI-NC adapts the Echo State Network (ESN) to enable fast online training without additional training overhead. Furthermore, it is general enough to be used for any types of power amplifiers (PAs) with different non-linearity characteristics and different channel environments. It can also be used for the communication system using multiple antennas and supporting multiple users simultaneously. Simulation results and hardware-based tests show that the proposed AI-NC can drastically improve the link performance and/or coverage of higher order modulations in practice.
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关键词
Nonlinear distortion,Training,Modulation,Radio frequency,Wireless communication,Adaptation models,Uplink,Non-linear distortion,neural networks,echo state network,higher order modulation,machine learning
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