TY - JOUR
T1 - A Digital Signal Recovery Technique Using DNNs for LEO Satellite Communication Systems
AU - Zhang, Yufeng
AU - Shen, Ming
A2 - Wang, Zhugang
A2 - Huang, Yonghui
A2 - Wei, Wei
A2 - Pedersen, Gert Frølund
PY - 2021
Y1 - 2021
N2 - This article proposes a new digital signal recovery (DSR) technique for next-generation power efficient low Earth orbit (LEO) satellite-To-ground communication systems, which feature additive white Gaussian noise (AWGN) channel and significant power variation. This technique utilizes the prior knowledge [i.e., nonlinearities of radio frequency power amplifiers (RF-PAs)] of space-borne transmitters to improve the quality of the signal received at ground stations by modeling and mitigating the imperfection using deep neural networks (DNNs). Benefiting from its robustness against noise and power variation, the proposed DNN based DSR technique (DNN-DSR), can correct high signal distortions caused by the nonlinearities and hence allows RF-PAs to work close to their saturation region, leading to a high power efficiency of the LEO satellites. This work has been validated by both simulations and experiments, in comparison with the power back-off technique as well as memory polynomial-based DSR solutions. Experimental results show that the DNN-DSR technique can increase the drain efficiency of the space-borne RF-PA from 32.6% to 45% while maintaining the same error vector magnitude as the power back-off technique. It has also been demonstrated that the proposed DNN-DSR technique can handle a signal power variation of 12 dB, which is challenging for conventional solutions.
AB - This article proposes a new digital signal recovery (DSR) technique for next-generation power efficient low Earth orbit (LEO) satellite-To-ground communication systems, which feature additive white Gaussian noise (AWGN) channel and significant power variation. This technique utilizes the prior knowledge [i.e., nonlinearities of radio frequency power amplifiers (RF-PAs)] of space-borne transmitters to improve the quality of the signal received at ground stations by modeling and mitigating the imperfection using deep neural networks (DNNs). Benefiting from its robustness against noise and power variation, the proposed DNN based DSR technique (DNN-DSR), can correct high signal distortions caused by the nonlinearities and hence allows RF-PAs to work close to their saturation region, leading to a high power efficiency of the LEO satellites. This work has been validated by both simulations and experiments, in comparison with the power back-off technique as well as memory polynomial-based DSR solutions. Experimental results show that the DNN-DSR technique can increase the drain efficiency of the space-borne RF-PA from 32.6% to 45% while maintaining the same error vector magnitude as the power back-off technique. It has also been demonstrated that the proposed DNN-DSR technique can handle a signal power variation of 12 dB, which is challenging for conventional solutions.
KW - Deep learning
KW - deep neural network (DNN)
KW - digital signal recovery (DSR)
KW - linearization
KW - low Earth orbit (LEO)
KW - power amplifier (PA)
KW - radio frequency (RF)
KW - satellite-To-ground communication
UR - http://www.scopus.com/inward/record.url?scp=85085771903&partnerID=8YFLogxK
U2 - 10.1109/TIE.2020.2994873
DO - 10.1109/TIE.2020.2994873
M3 - Journal article
SN - 0278-0046
VL - 68
SP - 6141
EP - 6151
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
IS - 7
M1 - 9097410
ER -