Intelligent Optimization of Wireless Access Point Deployment for Communication-Based Train Control Systems Using Deep Reinforcement Learning
This provides a scalable and data-efficient solution for next-generation CBTC systems in complex tunnel environments, though it appears to be an incremental improvement combining existing techniques.
The paper tackles the problem of optimizing wireless access point deployment in tunnel environments for communication-based train control systems by proposing a deep reinforcement learning framework that integrates parabolic wave equation channel modeling, conditional GAN data augmentation, and dueling DQN. The method outperforms conventional optimizers and traditional DQN, achieving higher average received power, better worst-case coverage, and improved computational efficiency.
Urban railway systems increasingly rely on communication based train control (CBTC) systems, where optimal deployment of access points (APs) in tunnels is critical for robust wireless coverage. Traditional methods, such as empirical model-based optimization algorithms, are hindered by excessive measurement requirements and suboptimal solutions, while machine learning (ML) approaches often struggle with complex tunnel environments. This paper proposes a deep reinforcement learning (DRL) driven framework that integrates parabolic wave equation (PWE) channel modeling, conditional generative adversarial network (cGAN) based data augmentation, and a dueling deep Q network (Dueling DQN) for AP placement optimization. The PWE method generates high-fidelity path loss distributions for a subset of AP positions, which are then expanded by the cGAN to create high resolution path loss maps for all candidate positions, significantly reducing simulation costs while maintaining physical accuracy. In the DRL framework, the state space captures AP positions and coverage, the action space defines AP adjustments, and the reward function encourages signal improvement while penalizing deployment costs. The dueling DQN enhances convergence speed and exploration exploitation balance, increasing the likelihood of reaching optimal configurations. Comparative experiments show that the proposed method outperforms a conventional Hooke Jeeves optimizer and traditional DQN, delivering AP configurations with higher average received power, better worst-case coverage, and improved computational efficiency. This work integrates high-fidelity electromagnetic simulation, generative modeling, and AI-driven optimization, offering a scalable and data-efficient solution for next-generation CBTC systems in complex tunnel environments.