High-Power Amplifier Pre-distorter Based on Neural Networks for 5G Satellite Communications

Authors: Abdelhamid Louliej, Younes Jabrane

Abstract:

Satellites are becoming indispensable assets to fifth-generation (5G) new radio architecture, complementing wireless and terrestrial communication links. The combination of satellites and 5G architecture allows consumers to access all next-generation services anytime, anywhere, including scenarios, like traveling to remote areas (without coverage). Nevertheless, this solution faces several challenges, such as a significant propagation delay, Doppler frequency shift, and high Peak-to-Average Power Ratio (PAPR), causing signal distortion due to the non-linear saturation of the High-Power Amplifier (HPA). To compensate for HPA non-linearity in 5G satellite transmission, an efficient pre-distorter scheme using Neural Networks (NN) is proposed. To assess the proposed NN pre-distorter, two types of HPA were investigated: Travelling Wave Tube Amplifier (TWTA) and Solid-State Power Amplifier (SSPA). The results show that the NN pre-distorter design presents an Error Vector Magnitude (EVM) improvement by 95.26%. Normalized Mean Square Error (NMSE) and Adjacent Channel Power Ratio (ACPR) were reduced by -43,66 dB and 24.56 dBm, respectively. Moreover, the system suffers no degradation of the Bit Error Rate (BER) for TWTA and SSPA amplifiers.

Keywords: Satellites, 5G, Neural Networks, High-Power Amplifier, Travelling Wave Tube Amplifier, Solid-State Power Amplifier, EVM, NMSE, ACPR.

References:

[1] A. Ghosh, A. Maeder, M. Baker and D. Chandramouli, "5G Evolution: A View on 5G Cellular Technology Beyond 3GPP Release 15," in IEEE Access, vol. 7, pp. 127639-127651, 2019.
[2] L. Chettri and R. Bera, "A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems," in IEEE Internet of Things Journal, vol. 7, no. 1, pp. 16-32, Jan. 2020.
[3] J. Li, D. Wang, L. Liu, B. Wang and C. Sun, "Satellite Ephemeris Broadcasting Architecture for 5G Integrated LEO Satellite Internet," 2022 IEEE 22nd International Conference on Communication Technology (ICCT), Nanjing, China, 2022, pp. 1437-1441.
[4] H.-L. Maattanen et al., "5G NR Communication over GEO or LEO Satellite Systems: 3GPP RAN Higher Layer Standardization Aspects," 2019 IEEE Global Communications Conference, pp. 1-6, 2019.
[5] 3GPP, "TS 23.501 V16.1.0; System Architecture for the 5G System," Jun. 2019.
[6] A. Maeder et.al., “Towards an Architecture Vision for Beyond 5G,” 6G Conference, Levi, Finland, Mar. 2019.
[7] B. Bertenyi, “5G Standardization update,” 3GPP Webinar, Jul. 2019. Online. Available: https://www.3gpp.org/news-events/2058-ran-rel-16-progress-and-rel-17-potential-work-areas, Aug. 3, 2019.
[8] X. Jing, H. Wang, F. You, Y. Wei, L. Chen and Y. Yang, "A Comparison of SSPA and TWTA for Beidou Navigation Satellite System," 2021 International Conference on Microwave and Millimeter Wave Technology (ICMMT), Nanjing, China, 2021, pp. 1-3.
[9] E. K. Mensah, T. A. Ashitei and J. K. Arthur, "Linearization of High Power Amplifier Using A Memristor in Microwave Transmission," 2019 International Conference on Communications, Signal Processing and Networks (ICCSPN), Accra, Ghana, 2019, pp. 1-7.
[10] K. Singh, M. R. Bharti and S. Jamwal, "A modified PAPR reduction scheme based on SLM and PTS techniques," International Conference on Signal Processing, Computing and Control, Solan, 2012, pp. 1-6.
[11] B. M. Kang, H. -G. Ryu and S. B. Ryu, "A PAPR Reduction Method using New ACE (Active Constellation Extension) with Higher Level Constellation," 2007 IEEE International Conference on Signal Processing and Communications, Dubai, United Arab Emirates, 2007, pp. 724-727.
[12] Sairam Vamsi, T., Terlapu, S.K., Vamshi Krishna, M. (2022). PAPR Analysis of FBMC and UFMC for 5G Cellular Communications. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore.
[13] Maheswari, M., Nagarajan, N.R., Banupriya, M. (2020). “Performance Analysis of UFMC System with Different Prototype Filters for 5G Communication,” International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019.
[14] Zhang Jian feng, Cheng Jian, Liu Bo and Yue Qiang, "Performance comparison of phase modulated system based on DSSS with HPA nonlinearity," IEEE International Conference on Wireless Communications, Networking and Information Security, Beijing, 2010, pp. 13-17.
[15] Anderson J A. “An Introduction to Neural Networks,” MIT Press, Cambridge, 1995.
[16] M. k. Alsmadi, K. B. Omar, S. A. Noah and I. Almarashdah, "Performance Comparison of Multi-layer Perceptron (Back Propagation, Delta Rule and Perceptron) algorithms in Neural Networks," 2009 IEEE International Advance Computing Conference, Patiala, India, 2009, pp. 296-299.
[17] A. Ranganathan, “The Levenberg-Marquardt Algorithm,” Honda Research Institute USA, 8 June 2004, Retrieved 12 August 2012.
[18] A. Kuh, "Mean squared error analysis of analog neural networks subject to drifting targets and noise," Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), Pacific Grove, CA, USA, 1998, pp. 683-684 vol.1.
[19] P. Händel, "Understanding Normalized Mean Squared Error in Power Amplifier Linearization," in IEEE Microwave and Wireless Components Letters, vol. 28, no. 11, pp. 1047-1049, Nov. 2018.
[20] R. Schmogrow et al., "Error Vector Magnitude as a Performance Measure for Advanced Modulation Formats," in IEEE Photonics Technology Letters, vol. 24, no. 1, pp. 61-63, Jan.1, 2012.
[21] Fu-Ling Lin, Shin-Fu Chen, Liang-Fang Chen and Huey-Ru Chuang, "Computer simulation and measurement of error vector magnitude (EVM) and adjacent-channel power ratio (ACPR) for digital wireless communication RF power amplifiers," IEEE VTS 50th Vehicular Technology Conference, Amsterdam, 1999, pp. 2024-2028 vol.4.