论文标题
在车辆OCC中的多代理深钢筋学习
Multi-Agent Deep Reinforcement Learning in Vehicular OCC
论文作者
论文摘要
光学相机通信（OCC）已成为未来自动驾驶汽车无缝操作的关键启用技术。在本文中，我们在车辆OCC中引入了一种光谱效率优化方法。具体而言，我们旨在最佳地调整调制顺序和相对速度，同时尊重位错误率和延迟约束。由于优化问题是NP难题，因此我们将优化问题建模为马尔可夫决策过程（MDP），以实现可以在线应用的解决方案。然后，我们通过采用拉格朗日放松方法来放松受约束的问题，然后再通过多代理深入强化学习（DRL）解决问题。我们通过广泛的模拟来验证我们提出的方案的性能，并将其与我们的方法的各种变体和随机方法进行比较。评估表明，与比较方案相比，我们的系统达到的总和光谱效率明显更高。
Optical camera communications (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. In this paper, we introduce a spectral efficiency optimization approach in vehicular OCC. Specifically, we aim at optimally adapting the modulation order and the relative speed while respecting bit error rate and latency constraints. As the optimization problem is NP-hard problem, we model the optimization problem as a Markov decision process (MDP) to enable the use of solutions that can be applied online. We then relaxed the constrained problem by employing Lagrange relaxation approach before solving it by multi-agent deep reinforcement learning (DRL). We verify the performance of our proposed scheme through extensive simulations and compare it with various variants of our approach and a random method. The evaluation shows that our system achieves significantly higher sum spectral efficiency compared to schemes under comparison.
