Algoritma Epsilon Greedy pada Reinforcement Learning untuk Modulasi Adaptif Komunikasi Vehicle to Infrastructure (V2I)

NAZMIA KURNIAWATI, YULI KURNIA NINGSIH, SOFIA DEBI PUSPA, TRI SWASONO ADI

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ABSTRAK

Komunikasi Vehicle to Infrastructure (V2I) memungkinkan kendaraan dapat terhubung ke berbagai macam infrastruktur. Dengan kondisi kendaraan yang bergerak, maka kondisi lingkungan yang dilewati mempengaruhi parameter komunikasi. Implementasi modulasi adaptif pada skema V2I memperbolehkan sistem menggunakan skema modulasi yang berbeda untuk mengakomodasi perubahan kondisi lingkungan. Pada penelitian ini digunakan skema modulasi QPSK, 8PSK, dan 16-QAM dengan memanfaatkan reinforcement learning dan algoritma epsilon greedy untuk menentukan skema modulasi yang digunakan berdasarkan level AWGN. Dari hasil simulasi dengan kondisi nilai epsilon yang divariasikan dari 0.1 hingga 0.5 didapatkan bahwa semakin tinggi nilai epsilon maka semakin sering agen tidak memilih skema modulasi dengan reward tertinggi.

Kata kunci: Reinforcement learning, Modulasi Adaptif, AWGN

 

ABSTRACT

Vehicle to Infrastructure (V2I) communication allows vehicles to be connected to various infrastructures. Under the scenario of a moving vehicle, the environmental conditions which is passed by the vehicle will affect the communication parameters. The adaptive modulation implementation in the V2I scheme allows the system to use different modulation schemes to accommodate changing environmental conditions. In this study, the QPSK, 8PSK, and 16-QAM modulation schemes were used by utilizing reinforcement learning and the epsilon greedy algorithm to determine the modulation scheme used based on AWGN level. From the simulation results with the conditions of the epsilon value varying from 0.1 to 0.5, it is found that the higher the epsilon value, the more often the agent does not choose the modulation scheme with the highest reward.

Keywords: Reinforcement learning, Adaptive Modulation, AWGN


Kata Kunci


Reinforcement learning; Modulasi Adaptif; AWGN

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Referensi


Bliss, D. W., & GovindasamySiddhartan. (2013). Adaptive Wireless Communications (MIMO Channels and Networks) (1st Editio). Cambridge University Press.

Cronin, B. (2015). Connected Vehicle Benefits. Bureau of Transportation Statistics. https://www.its.dot.gov/factsheets/pdf/ConnectedVehicleBenefits.pdf

Dangi, M., & Porwal, M. K. (2015). Analyses of SNR Threshold for Minimum BER in Various Modulations Schemes and Development Of an Adaptive Modulation Scheme. IJISET - International Journal of Innovative Science, Engineering & Technology, 2(3), 139–142.

dos Santos Mignon, A., & de Azevedo da Rocha, R. L. (2017). An Adaptive Implementation of ε-Greedy in Reinforcement Learning. Procedia Computer Science, 109, 1146–1151. https://doi.org/10.1016/j.procs.2017.05.431

Eska, A. C. (2018). Adaptive Modulation and Coding (AMC) around Building Environment for MS Communication at The Train. EMITTER International Journal of Engineering Technology, 6(2), 386–394. https://doi.org/10.24003/emitter.v6i2.279

Halegoua, G. R. (2020). Smart Cities. The MIT Press.

Ippolito Jr., L. J. (2017). Satellite Communications Systems Engineering: Atmospheric Effects, Satellite Link Design and System Performance (2nd Editio). Wiley.

Lowe, R., & Ziemke, T. (2013). Exploring the relationship of reward and punishment in reinforcement learning. 2013 IEEE Symposium on Adaptive Dynamic Programming and

Reinforcement Learning (ADPRL), 140–147. https://doi.org/10.1109/ADPRL.2013.6615000

Masood, R. F. (2013). Adaptive Modulation (QPSK, QAM). ArXiv Preprint ArXiv:1302.7145. http://arxiv.org/abs/1302.7145

Nieuwdorp, T. (2017). Dare to Discover: The Effect of the Exploration Strategy on an Agent’s Performance. Radboud University Nijmegen.

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

Oyetola, O. K., Okubanjo, A. A., Okandeji, A. A., Alao, P. O., Osifeko, M. O., & Olasunkanmi, O. G. (2018). SYMBOL ERROR PROBABILITY OF 16-QAM SYSTEM OVER AWGN AND RAYLEIGH FADING CHANNELS. African Journal of Science & Nature, 7, 29–39.

Pandey, R., Awasthi, A., & Srivastava, V. (2013). Comparison between Bit Error Rate And Signal To Noise Ratio in OFDM Using LSE Algorithm. Conference on Advances in Communication and Control Systems 2013 (CAC2S 2013), 463–466.

Ravinchandiran, S. (2018). Hands-On Reinforcement Learning with Python. Packt Publishing Ltd.

Rochmatika, R. A., Suryani, T., & Wirawan. (2018). Performance of Adaptive Modulation over Frequency Selective Fading Channel in VANET Environment. 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), 400–405.

Sassi, A., Charfi, F., Kamoun, L., Elhillali, Y., & Rivenq, A. (2012). OFDM Transmission Performance Evaluation in V2X Communication. International Journal of Computer Science Issues, 9(2), 141–148. http://arxiv.org/abs/1410.8039

Sighal, M., Agarwal, M., Trikha, M., & Sharma, N. (2013). Bit Error Rate Performance of Gray Coded 8-PSK. MIT International Journal of Electronics and Communication Engineering, 3(1), 20–24.

Singya, P. K., Shaik, P., Kumar, N., Bhatia, V., & Alouini, M.-S. (2021). A Survey on Higher-Order QAM Constellations: Technical Challenges, Recent Advances, and Future Trends. IEEE Open Journal of the Communications Society, 1–1. https://doi.org/10.1109/OJCOMS.2021.3067384

Skrucany, T., Sarkan, B., FigluzTomasz, Synak, F., & Vrabel, J. (2017). Measuring of noise emitted by moving vehicles. Dynamics of Civil Engineering and Transport Structures and Wind Engineering – DYN-WIND’2017, 107. https://doi.org/10.1051/matecconf/2017107000

Sutton, R. S., & Barto, A. G. (2015). Reinforcement Learning: An Introduction. The MIT Press.

Thomas, B. (2016). Proposed rule would mandate vehicle-to-vehicle (V2V) communication on light vehicles, allowing cars to “talk†to each other to avoid crashes. National Highway Traffic Safety Information.

Wietfeld, C., & Ide, C. (2015). Vehicle-to-infrastructure communications. In Vehicular Communications and Networks (pp. 3–28). Elsevier. https://doi.org/10.1016/B978-1-78242-211-2.00001-5




DOI: https://doi.org/10.26760/elkomika.v9i3.716

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