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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Yufeng Zhang Zhugang Wang Yonghui Huang Jian Ren Yingzeng Yin Ying Liu Gert Frolund Pedersen Ming Shen |
| Abstract | This paper proposes a novel deep neural network (DNN)-based receiver for next-generation low Earth orbit (LEO) satellite communications. The DNN receiver can concurrently compensate for multiple imperfections of the satellite communication system to improve the quality of satellite-to-ground transmission. A special focus has been placed on handling the nonlinear distortion in the transmitted signal caused by space-borne high-efficiency radio frequency power amplifiers (RF-PAs), which is crucial in high-throughput satellite communications, but has been overlooked by existing relevant research. In this receiver, a DNN is designed and trained to learn the channel effects, nonlinearities of the RF-PAs, and digital modulation schemes in the received signal for demodulation and nonlinearity/channel effect compensation at the same time. The proposed receiver has been evaluated using five popular filtered orthogonal frequency division modulations with the nonlinear distortions experimentally extracted from a real gallium nitride (GaN) RF-PA and the additive white Gaussian noise channel generated by simulations. The validation results demonstrate that the DNN receiver can accommodate different modulation schemes and two typical groups of RF-PA classes with a satisfactory bit error rate performance. It has the potential to boost the performance of existing on-orbit LEO satellite communication systems with minimal system modifications and serves as a promising solution for future satellite communication services. |
| e-ISSN | 21693536 |
| DOI | 10.1109/ACCESS.2020.3044321 |
| Journal | IEEE Access |
| Volume Number | 8 |
| Language | English |
| Publisher | IEEE |
| Publisher Date | 2020-01-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Deep Neural Network Filtered-ofdm Radio Frequency Power Amplifier Nonlinear Distortion Digital Signal Recovery Demodulation |
| Content Type | Text |
| Resource Type | Article |
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