Research Article
PAPR Reduction Scheme for Localized SC-FDMA Based on Deep Learning
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@INPROCEEDINGS{10.1007/978-3-030-93398-2_60, author={Hao Lu and Yu Zhou and Yue Liu and Rui Li and Ning Cao}, title={PAPR Reduction Scheme for Localized SC-FDMA Based on Deep Learning}, proceedings={Wireless and Satellite Systems. 12th EAI International Conference, WiSATS 2021, Virtual Event, China, July 31 -- August 2, 2021, Proceedings}, proceedings_a={WISATS}, year={2022}, month={1}, keywords={SC-LFDMA AE DNN HPA}, doi={10.1007/978-3-030-93398-2_60} }- Hao Lu
Yu Zhou
Yue Liu
Rui Li
Ning Cao
Year: 2022
PAPR Reduction Scheme for Localized SC-FDMA Based on Deep Learning
WISATS
Springer
DOI: 10.1007/978-3-030-93398-2_60
Abstract
Large peak-to-average power ratio (PAPR) hinders the development of the localized single carrier frequency division multiple access (SC-LFDMA). In this paper, autoencoder (AE) is introduced in SC-LFDMA to reduce PAPR, known as AE-SC-LFDMA. In AE-SC-LFDMA, the Encoder and Decoder of AE are used to encode and decode the modulated symbols of conventional SC-LFDMA based on deep neural network (DNN). This process aims to make AE-SC-LFDMA achieve lower PAPR as well as be more robust to the nonlinear distortion (NLD) of high power amplifier (HPA). Simulation results show that the proposed scheme outperforms conventional schemes both in bit error rate (BER) and PAPR.
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