Joint PAPR reduction and digital post-distortion transmission framework for nonlinear OFDM systems
Published 16 October, 2026
Orthogonal Frequency-Division Multiplexing (OFDM) signals inherently exhibit a high Peak-to-Average Power Ratio (PAPR). This forces the Power Amplifier (PA) to operate with large input back-off, resulting in substantial energy waste and posing a serious challenge for green communication.
A new study in Digital Communications and Networks proposes a novel transmission framework. For the first time, this framework integrates the PAPR reduction technique with the DPoD-based method. Consequently, it achieves a better balance between PA efficiency and BER performance in OFDM systems. On this basis, a novel receiver-side algorithm, namely SDFNE, was developed to jointly mitigate nonlinear distortion caused by both companding operations and PA nonlinearity.
Comprehensive simulations were conducted to verify the effectiveness and superiority of the proposed method against several relevant benchmarks, including ALC, ideal DPD, and SPs-ILSE. “Our results demonstrated that SDFNE achieves impressive BER performance compared to all considered counterparts under various PA distortion models, including both AM-AM and AM-PM types. Besides, a perfect estimate is unattainable in practice,” shares co-corresponding author Sai Huang.
While a slight performance gap exists between the estimated and ideal parameters, the proposed method nonetheless demonstrated clear superiority over the competing techniques.
“Going forward, several promising directions can be explored within the proposed transmission framework,” says Huang. “For example, machine learning-based approaches could be leveraged to address this issue.”
Promising candidates include deep neural networks and model-driven unfolding architectures.
“These methods can further reduce the computational complexity of nonlinearity equalization while improving adaptability to time-varying PA characteristics,” says lead and co-corresponding author Yi Fang. “Adaptive feedback mechanisms that dynamically adjust the companding parameters and iteration count based on channel conditions could also enhance the robustness of the framework.”
Contact the author:
Sai Huang
Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Funder:
This work was supported by the National Natural Science Foundation of China under Grant (62422103, 62321001, 62171045, 62201090); The Open Project of Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, No. MMC202403.
Conflict of interest:
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
See the article:
https://doi.org/10.1016/j.dcan.2026.07.002