A novel method for reducing the effect of doppler frequency shift in high-speed railway mobile communication using machine learning
Email:
trinhhuong@utc.edu.vn
Từ khóa:
Doppler compensation, High Speed Railway, Doppler, Carrier Frequency Offset, Machine Learning, Deep Learning
Tóm tắt
Currently, high-speed railway systems are rapidly expanding worldwide, necessitating reliable and efficient mobile communication solutions. However, the Doppler frequency shift caused by the high speeds of trains presents significant challenges to communication systems, particularly those using OFDM in LTE. This paper presents a novel for Doppler frequency compensation in high-speed railway communication based on the results of estimating the train's velocity using machine learning algorithms. By leveraging advanced algorithm such as neural networks, our method dynamically predicts and compensates for Doppler shifts in real-time. In this proposed novel, the Doppler frequency value is calculated based on the actual train’s velocity and the scenario of a high-speed railway, then the Doppler frequency is used to compensate the carrier frequency offset (CFO) directly at the Access Point (AP) device on the train. We conduct simulations to evaluate the effectiveness of our proposed solution in a high-speed railway scenario. The results demonstrate a marked improvement in communication reliability and data integrity, highlighting the potential of machine learning to enhance the performance of mobile communication systems in high-speed railways. The results of the proposed model are evaluated based on the system’s bit error rate BER after the CFO compensation decreases and the ratio of average energy per bit (Eb) to noise power density (N0) (Eb/N0) increases. This shows that the proposed solution works effectively and reduces the system’s bit errors while improving the communication performance. This study opens new avenues for integrating intelligent systems in transportation networks, ensuring seamless connectivity, and improving passenger experienceTài liệu tham khảo
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[2]. W. Zhang, Y. Zeng, J. li, A review of vehicle system dynamics in the development of high-speed trains in China, Int. J. Dynam. Control, 1 (2013) 81–97. https://doi.org/10.1007/s40435-013-0005-1
[3]. T. Xu, D. Gao, P. Dong, C. H. Foh, H. Zhang, V. C. M. Leung, Improving the Security of Wireless Communications on High-Speed Trains by Efficient Authentication in SCN-R, in IEEE Transactions on Vehicular Technology, 68 (2019) 7283-7295. https://doi.org/10.1109/TVT.2019.2920988
[4]. H. Hofestadt, GSM-R: global system for mobile radio communications for railways, 1995 International Conference on Electric Railways in a United Europe, Amsterdam, Netherlands, (1995) 111-115. https://doi.org/10.1049/cp:19950189
[5]. Q. Du, G. Wu, Q. Yu, S. Li, ICI mitigation by Doppler frequency shift estimation and pre-compensation in LTE-R systems, 2012 1st IEEE International Conference on Communications in China (ICCC), Beijing, China, (2012) 469-474. https://doi.org/10.1109/ICCChina.2012.6356928
[6]. Dian Fan, Z. Zhong, Gongpu Wang, Feifei Gao, Doppler shift estimation for high-speed railway wireless communication systems with large-scale linear antennas, 2015 International Workshop on High Mobility Wireless Communications (HMWC), Xi'an, China, (2015) 96-100. https://doi.org/ 10.1109/HMWC.2015.7354343
[7]. Fan Bai, H. Krishnan, Reliability Analysis of DSRC Wireless Communication for Vehicle Safety Applications, 2006 IEEE Intelligent Transportation Systems Conference, Toronto, ON, Canada, (2006) 355-362. https://doi.org/10.1109/ITSC.2006.1706767
[8]. A. Elbery, S. Sorour, H. Hassanein, A. B. Sediq, H. Abou-zeid, To DSRC or 5G? A Safety Analysis for Connected and Autonomous Vehicles, 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, (2021) 1-6. https://doi.org/10.1109/GLOBECOM46510.2021.9685065
[9]. H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, N. D. Sidiropoulos, Learning to Optimize: Training Deep Neural Networks for Interference Management, in IEEE Transactions on Signal Processing, 66 (2018) 5438-5453. https://doi.org/10.1109/TSP.2018.2866382
[10]. H., Cho, T., Nguyen, H., Nguyen et al, A robust ICI suppression based on an adaptive equalizer for very fast time-varying channels in LTE-R systems, J Wireless Com Network, 17 (2018). https://doi.org/10.1186/s13638-018-1026-4
[11]. Huong Trinh Thi, Nguyen Manh Dat, To Thi Thao, Nguyen Duy Viet, Vu Van Yem, Compensating Doppler Frequency Shift of High-Speed Rail Communications, International Journal of Applied Engineering Research, 13 (2018) 13344-13348.
[12]. Huong Trinh Thi, Nguyen Ngoc Huy, Pham Duy Phong, Nguyen Duy Viet, Vu Van Yem, Novel Method Using CP for Estimation and Compensation of CFO in HSR Communications, European Journal of Electrical and Computer Engineering, (2018).
[13]. V. Van Yem, T.T. Huong, ICI Mitigation by Estimation of Double Carrier Frequency Offsets in High-Speed-Railway Communication Systems for Smart Cities, Mobile Netw Appl, 23 (2018) 1563–1571. https://doi.org/10.1007/s11036-017-0990-y
[14]. P. Kyosti, J. Meinila, L. Hentila, X Zhao, Winner II Channel models: Part1 channel models version 1.2. WINNER and Information Society Technologies, Technical Report (2007).
[15]. Y. Sun, C.-Y. Lee, J.-m. Jo, J.-h. Lee, Y.-J. Han, Study on the effectiveness of high-speed railway communication and signaling system based on 4G LTE technology, 2013 13th International Conference on Control, Automation and Systems, (2013) 402-406, https://doi.org/10.1109/ICCAS.2013.6703988
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Nhận bài
01/08/2024
Nhận bài sửa
05/09/2024
Chấp nhận đăng
10/09/2024
Xuất bản
15/09/2024
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Kiểu trích dẫn
Trinh Thi, H., & Nguyen Duc, A. (1726333200). A novel method for reducing the effect of doppler frequency shift in high-speed railway mobile communication using machine learning. Tạp Chí Khoa Học Giao Thông Vận Tải, 75(7), 2104-2118. https://doi.org/10.47869/tcsj.75.7.8
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