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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika
ADI, TRI SWASONO
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Electrical & Electronics Engineering Engineering

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Algoritma Epsilon Greedy pada Reinforcement Learning untuk Modulasi Adaptif Komunikasi Vehicle to Infrastructure (V2I) KURNIAWATI, NAZMIA; NINGSIH, YULI KURNIA; PUSPA, SOFIA DEBI; ADI, TRI SWASONO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 3: Published July 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i3.716

Abstract

ABSTRAKKomunikasi 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 ABSTRACTVehicle 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
Co-Authors Kurniawati, Nazmia Puspa, Sofia Debi Yuli Kurnia Ningsih