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All Journal Emerging Science Journal
Ratna Mayasari
1) School of Electrical Engineering and Informatics, Bandung Institute of Technology (ITB), Bandung 40132, Indonesia. 3) Center of Excellence for Intelligent Sensing-IoT, Research Institute of Sustainable Society, Telkom University, Bandung 40257
Environmental Science

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Dual-Agent Q-Learning for Cross-Layer IEEE 802.11bd Optimization in Dense VANETs Galih Nugraha Nurkahfi; Suyoto; Agus Subekti; Budi Prawara; Ratna Mayasari; Andy Triwinarko; Nasrullah Armi; Eueung Mulyana; Nana Rachmana Syambas
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-019

Abstract

Dense vehicular ad hoc networks face critical challenges in reliably delivering safety messages due to channel congestion, packet collisions, and interference. This study develops a dual-agent Q-learning framework for cross-layer IEEE 802.11bd optimization to improve latency and power efficiency while maintaining acceptable packet delivery ratios in dense traffic. We propose a decomposed architecture separating PHY-layer power control and MAC-layer beacon rate adaptation, with deterministic SINR-based MCS selection ensuring IEEE 802.11bd compliance. The framework is evaluated using a Python-based VANET simulator implementing the IEEE 802.11bd PHY/MAC stack with realistic SUMO mobility, multi-class background traffic, and omnidirectional/sectoral antennas across 20-90 vehicles/km densities. Results show dual-agent Q-learning reduces average latency by 44.6% (31.1ms to 17.2ms) and transmission power by 55% (15-20dBm to 9dBm) compared to static baselines, with acceptable 5-11% PDR reduction (94.2% to 88.6%). The approach converges within 8,500 episodes, significantly faster than single-agent Q-learning (12,500) and dual-agent DQN (14,000-35,000). This work introduces the first dual-agent tabular Q-learning for joint power-rate-MCS optimization in IEEE 802.11bd VANETs, demonstrating that agent decomposition reduces state-action complexity while enabling interpretable, fast-converging control suitable for sub-100ms vehicular applications.
Co-Authors Agus Subekti Andy Triwinarko Budi Prawara Eueung Mulyana Galih Nugraha Nurkahfi Nana Rachmana Syambas Nasrullah Armi Suyoto