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All Journal Emerging Science Journal
Galih Nugraha Nurkahfi
1) School of Electrical Engineering and Informatics, Bandung Institute of Technology (ITB), Bandung 40132, Indonesia. 2) Research Organization of Electronics and Informatics, National Research and Innovation Agency (BRIN), Bandung 40135
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 Nana Rachmana Syambas Nasrullah Armi Ratna Mayasari Suyoto