Deep Digital Signal Recovery for LEO Satellite Communication in Presence of System Perturbations

Jakob Gjedsted Brask, Kasper Bruun Olesen, Arun Yadav, Lauge Føns Dyring, Feridoon Jalili, Yufeng Zhang, Ming Shen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review


This paper proposes a deep neural network (DNN)based digital signal recovery (DSR) technique for low Earth orbit (LEO) satellite communications. Different from existing work, which only investigates impacts of the satellite-to-ground communication channel, this work focuses on handling the nonlinearity variations caused by input power level perturbations in the transmitter. The system is validated using a high gain radio frequency power amplifier(RF-PA) operating at 28.5 GHz, where perturbations are introduced by varying the power level of the input signal, from −39 dBm to −31 dBm, to the RFPA. Experimental results show that the DNN trained at an input power level of−35 dBm achieved an improvement of 7.52 dB in the adjacent channel leakage ratio (ACLR), and an improvement of 4.2% in error vector magnitude (EVM). Applying the DDN trained at −35 dBm to other cases demonstrates that a 1 dB power level perturbation only leads to ≈ 1 dB degradation of the ACLR and ≈ 1.6% degradation of the EVM, respectively, which indicates the potential of the proposed approach.
Original languageEnglish
Title of host publication2021 IEEE MTT-S International Wireless Symposium (IWS)
Number of pages3
Publication date10 Aug 2021
ISBN (Print)978-1-6654-3528-4
ISBN (Electronic)978-1-6654-3527-7
Publication statusPublished - 10 Aug 2021
Event2021 IEEE MTT-S International Wireless Symposium (IWS) - Nanjing, China
Duration: 23 May 202126 May 2021


Conference2021 IEEE MTT-S International Wireless Symposium (IWS)
SeriesIEEE MTT-S International Wireless Symposium (IWS)

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