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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References
Myung, H.G., Goodman, D.J.: Single Carrier FDMA: A New Air Interface for Long Term Evolution, vol. 8. Wiley, Hoboken (2008)
Chen, G., Song, S., Letaief, K.B.: A low-complexity precoding scheme for PAPR reduction in SC-FDMA systems. In: 2011 IEEE Wireless Communications and Networking Conference, pp. 1358–1362. IEEE (2011)
Ji, J., Ren, G., Zhang, H.: PAPR reduction of SC-FDMA signals via probabilistic pulse shaping. IEEE Trans. Veh. Technol. 64(9), 3999–4008 (2014)
Mohammad, A., Zekry, A., Newagy, F.: A time domain SLM for PAPR reduction in SC-FDMA systems. In: 2012 IEEE Global High Tech Congress on Electronics, pp. 143–147. IEEE (2012)
Meza, C.A., Lee, K., Lee, K.: PAPR reduction in single carrier FDMA uplink system using parametric linear pulses. In: ICTC 2011, pp. 424–429. IEEE (2011)
Kamal, S., Meza, C.A.A., Tran, N.H., Lee, K.: Low-PAPR hybrid filter for SC-FDMA. IEEE Commun. Lett. 21(4), 905–908 (2016)
Ye, H., Liang, L., Li, G.Y., Juang, B.: Deep learning-based end-to-end wireless communication systems with conditional GANs as unknown channels. IEEE Trans. Wirel. Commun. 19(5), 3133–3143 (2020)
Dörner, S., Cammerer, S., Hoydis, J., Ten Brink, S.: Deep learning based communication over the air. IEEE J. Sel. Top. Sig. Process. 12(1), 132–143 (2017)
O’shea, T., Hoydis, J.: An introduction to deep learning for the physical layer. IEEE Trans. Cogn. Commun. Networking 3(4), 563–575 (2017)
Kim, M., Lee, W., Cho, D.H.: A novel PAPR reduction scheme for OFDM system based on deep learning. IEEE Commun. Lett. 22(3), 510–513 (2017)
Huleihel, Y., Ben-Dror, E., Permuter, H.H.: Low PAPR waveform design for OFDM system based on convolutional auto-encoder. arXiv preprint arXiv:2011.06349 (2020)
Sohn, I.: A low complexity PAPR reduction scheme for OFDM systems via neural networks. IEEE Commun. Lett. 18(2), 225–228 (2014)
Felix, A., Cammerer, S., Dörner, S., Hoydis, J., Ten Brink, S.: OFDM-autoencoder for end-to-end learning of communications systems. In: 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5. IEEE (2018)
Xue, S., Ma, Y., Yi, N., Tafazolli, R.: Unsupervised deep learning for MU-SIMO joint transmitter and noncoherent receiver design. IEEE Wirel. Commun. Lett. 8(1), 177–180 (2018)
Wang, B., Si, Q., Jin, M.: A novel tone reservation scheme based on deep learning for PAPR reduction in OFDM systems. IEEE Commun. Lett. 24(6), 1271–1274 (2020)
Balti, E., Guizani, M.: Impact of non-linear high-power amplifiers on cooperative relaying systems. IEEE Trans. Commun. 65(10), 4163–4175 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Candreva, E.A., Tarchi, D., Vanelli-Coralli, A., Corazza, G.E.: Robust SC-FDMA subcarrier mapping for non-linear channels. In: 2014 7th Advanced Satellite Multimedia Systems Conference and the 13th Signal Processing for Space Communications Workshop (ASMS/SPSC), pp. 360–365. IEEE (2014)
Saleh, A.A.: Frequency-independent and frequency-dependent nonlinear models of TWT amplifiers. IEEE Trans. Commun. 29(11), 1715–1720 (1981)
Acknowledgements
This work was supported by Research on Performance Evaluation and Optimization Technology of Local IOT for Client-side Metering Equipment under grant No. 5700-202118203A-0-0-00.
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Lu, H., Zhou, Y., Liu, Y., Li, R., Cao, N. (2022). PAPR Reduction Scheme for Localized SC-FDMA Based on Deep Learning. In: Guo, Q., Meng, W., Jia, M., Wang, X. (eds) Wireless and Satellite Systems. WiSATS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 410. Springer, Cham. https://doi.org/10.1007/978-3-030-93398-2_60
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DOI: https://doi.org/10.1007/978-3-030-93398-2_60
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