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<p>With the rapid proliferation of connected vehicle technologies, Vehicular Ad-hoc Networks (VANETs) are facing significant challenges in managing network resources due to their highly dynamic topology and frequent link disruptions. The decentralized nature of traditional routing and scheduling protocols often leads to performance degradation, increased latency, and severe congestion. To address these issues, this paper proposes a novel, centralized, and intelligent architecture that leverages Software-Defined Networking (SDN) and the Proximal Policy Optimization (PPO) algorithm. In our framework, the SDN controller provides a global, real-time view of the network state, which serves as the input for a deep reinforcement learning agent based on PPO, which learns to make optimal scheduling and congestion control decisions across the network. Our simulation results, conducted in a realistic vehicular environment, demonstrate that the proposed SDN-PPO architecture significantly outperforms the decentralized PPO approach, SDN-DQN and IEEE 1609.4 in terms of key performance indicators, namely packet delivery rate, end-to-end latency, and throughput. This work shows that the strategic combination of a global SDN view with a stable and robust PPO agent is a highly effective solution for managing the complexity of dynamic vehicular environments.</p>

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Intelligent Congestion Control and Scheduling in VANETs: An SDN-Based PPO Approach

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Nouha Alyaoui,
  • Awatef Benfradj Guiloufi,
  • Oumayma Baccouch,
  • Karim Chabir

摘要

With the rapid proliferation of connected vehicle technologies, Vehicular Ad-hoc Networks (VANETs) are facing significant challenges in managing network resources due to their highly dynamic topology and frequent link disruptions. The decentralized nature of traditional routing and scheduling protocols often leads to performance degradation, increased latency, and severe congestion. To address these issues, this paper proposes a novel, centralized, and intelligent architecture that leverages Software-Defined Networking (SDN) and the Proximal Policy Optimization (PPO) algorithm. In our framework, the SDN controller provides a global, real-time view of the network state, which serves as the input for a deep reinforcement learning agent based on PPO, which learns to make optimal scheduling and congestion control decisions across the network. Our simulation results, conducted in a realistic vehicular environment, demonstrate that the proposed SDN-PPO architecture significantly outperforms the decentralized PPO approach, SDN-DQN and IEEE 1609.4 in terms of key performance indicators, namely packet delivery rate, end-to-end latency, and throughput. This work shows that the strategic combination of a global SDN view with a stable and robust PPO agent is a highly effective solution for managing the complexity of dynamic vehicular environments.