Penentuan Batas Gain Kanal Modulasi Adaptif V2V dengan Doppler Shift yang Bervariasi Menggunakan Machine Learning

Nazmia Kurniawati; Aisyah Novfitri; Arti Suryaning Tyas

doi:10.30998/faktorexacta.v15i4.14578

Penentuan Batas Gain Kanal Modulasi Adaptif V2V dengan Doppler Shift yang Bervariasi Menggunakan Machine Learning

Nazmia Kurniawati^(1*), Aisyah Novfitri⁽²⁾, Arti Suryaning Tyas⁽³⁾

(1) Politeknik Negeri Jakarta
(2) Institut Teknologi Telkom Jakarta
(3) Politeknik Negeri Jakarta
(*) Corresponding Author

Abstract

Doppler shift is a phenomenon that occurs when the vehicle is moving. The effect of Doppler shift is a degradation in performance of Vehicle to Vehicle (V2V) communication. Adaptive modulation is a technique to improve the performance. It is done by adjusting the modulation scheme used according to noise conditions while keeping the Bit Error Rate (BER) value not exceeding 10-3. In this research, three Doppler shift values are used. The shift is derived from speed limit determined by The Government of Indonesia. Then machine learning algorithm is used to predict channel gain threshold that can optimize the use of Signal to Noise Ratio (SNR) with a BER limit of 10-3. From the prediction results, it is found that by implementing the predicted channel gain threshold, the SNR required by adaptive modulation has the lowest value compared to non-adaptive modulation schemes. The lower the required SNR value, the communication is more resistant to noise interference.

Full Text:

PDF (Indonesian)

References

Y. Miao, X. Dong, M. A. Bourassa, and D. Zhu, “Effects of Different Wave Spectra on Wind-Wave Induced Doppler Shift Estimates,” in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Sep. 2020, pp. 5705–5708, doi: 10.1109/IGARSS39084.2020.9323413.

T. Gerkema, L. R. M. Maas, and H. van Haren, “A Note on the Role of Mean Flows in Doppler-Shifted Frequencies,” Journal of Physical Oceanography, vol. 43, no. 2, pp. 432–441, Feb. 2013, doi: 10.1175/JPO-D-12-090.1.

X. Shu, C. Li, W. Chen, J. Yu, and K. Yang, “Performance Analysis of V2V Radio Channel under Typical Urban Intersection Scenario,” in 2018 IEEE International Conference on Communication Systems (ICCS), 2018, pp. 216–220.

A. Sassi, F. Charfi, L. Kamoun, Y. Elhillali, and A. Rivenq, “OFDM Transmission Performance Evaluation in V2X Communication,” International Journal of Computer Science Issues, vol. 9, no. 2, pp. 141–148, Oct. 2012, [Online]. Available: http://arxiv.org/abs/1410.8039.

N. Kurniawati and Y. K. Ningsih, “Reinforcement Learning-Based Adaptive Modulation for Vehicular Communication,” in 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), May 2021, pp. 224–227, doi: 10.1109/ECTI-CON51831.2021.9454740.

A. Novfitri, T. Suryani, and Suwadi, “Performance Analysis of Vehicle-to-Vehicle Communication with Adaptive Modulation,” in 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Oct. 2018, pp. 187–191, doi: 10.1109/EECCIS.2018.8692895.

O. M. Abbas, “Forecasting with Machine Learning,” International Journal of Computer (IJC), vol. 26, no. 1, pp. 184–194, 2017.

K. Shailaja, B. Seetharamulu, and M. A. Jabbar, “Machine Learning in Healthcare: A Review,” in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Mar. 2018, pp. 910–914, doi: 10.1109/ICECA.2018.8474918.

P. Gogas and T. Papadimitriou, “Machine Learning in Economics and Finance,” Computational Economics, vol. 57, no. 1, pp. 1–4, Jan. 2021, doi: 10.1007/s10614-021-10094-w.

I. T. Nafea, “Machine Learning in Educational Technology,” in Machine Learning - Advanced Techniques and Emerging Applications, InTech, 2018.

W. Wang, H. Liu, W. Lin, Y. Chen, and J.-A. Yang, “Investigation on Works and Military Applications of Artificial Intelligence,” IEEE Access, vol. 8, pp. 131614–131625, 2020, doi: 10.1109/ACCESS.2020.3009840.

Indonesia, Peraturan Pemerintah Republik Indonesia Nomor 79 Tahun 2013 tentang Jaringan Lalu Lintas dan Angkutan Jalan. Indonesia, 2013.

F. Arena, G. Pau, and A. Severino, “A Review on IEEE 802.11p for Intelligent Transportation Systems,” Journal of Sensor and Actuator Networks, vol. 9, no. 2, p. 22, Apr. 2020, doi: 10.3390/jsan9020022.

A. Zajic, Mobile-to-Mobile Wireless Channels. Artech House, 2013.

A. M. S. Abdelgader and W. Lenan, “The Physical Layer of the IEEE 802.11p WAVE Communication Standard: The Specifications and Challenges,” 2014.

S. D. A. Bujang et al., “Multiclass Prediction Model for Student Grade Prediction Using Machine Learning,” IEEE Access, vol. 9, pp. 95608–95621, 2021, doi: 10.1109/ACCESS.2021.3093563.

N. Kurniawati, A. Fahmi, and S. Alam, “Predicting Rectangular Patch Microstrip Antenna Dimension Using Machine Learning,” Journal of Communications, vol. 16, no. 9, pp. 394–399, 2021, doi: 10.12720/jcm.16.9.394-399.

S. Theodoridis, “Mean-Square Error Linear Estimation,” in Machine Learning, Elsevier, 2020, pp. 121–177.

DOI: http://dx.doi.org/10.30998/faktorexacta.v15i4.14578

Refbacks

There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

View Faktor Exacta Stats

Username
Password
Remember me