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首页> 外文期刊>IEEE sensors journal >Intelligent Dynamic Spectrum Access Using Deep Reinforcement Learning for VANETs
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Intelligent Dynamic Spectrum Access Using Deep Reinforcement Learning for VANETs

机译：智能动态频谱访问使用深层加固学习vanets

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In vehicular ad hoc networks (VANETs), vehicles can communicate with other vehicles or devices through vehicle-to-X communication. However, with the rise of the Internet of Things, it is a real challenge to make the limited spectrum resource meet the increasing communication demands, i.e., the channel contention problem. To solve this problem, this paper proposes a strategy combining multi-hop forwarding through vehicles and dynamic spectrum access. Firstly, a group-based multi-hop broadcast protocol, G-hop, is proposed. G-hop classifies vehicles with similar characteristics such as moving speed and communication distance into same groups using the depth-first-search algorithm. Messages are forwarded within a group in priority and then across groups, which limits both the range and the number of relay vehicles, i.e., channel contenders. Further, we adopt deep reinforcement learning techniques to achieve dynamic spectrum access. We design a Global Optimization algorithm based on Experience Accumulation (GOEA) using deep reinforcement learning. In GOEA, a network structure combined with recurrent neural network and deep Q-network is proposed for learning the time-varying process, and then a reward method is applied to optimize the global utility. Vehicles that need to transmit messages select channels dynamically following the guidance of GOEA. The experimental results demonstrate that the G-hop protocol reduces the packet loss rate from 0.8 to about 0.1. Meanwhile, compared with Slotted Aloha and DQN, our GOEA algorithm reduces the collision probability and channel idle probability by 60%. Moreover, as the number of vehicles increases, the transmission success rate can be improved by 20%.
机译：在车辆临时网络（VANET）中，车辆可以通过车辆到X通信与其他车辆或设备通信。然而，随着事物互联网的升高，使有限的频谱资源满足越来越多的通信需求，即渠道争用问题是一个真正的挑战。为了解决这个问题，本文提出了一种通过车辆和动态频谱访问结合多跳转发的策略。首先，提出了一种基于组的多跳广播协议G-Hop。 G跳通过使用深度第一搜索算法对具有类似特性的车辆进行类似特性的车辆，例如将移动速度和通信距离相同的组。消息在优先级的组中转发，然后在跨组中转发，这限制了中继车辆的范围和数量，即频道竞争者。此外，我们采用深度加固学习技术来实现动态频谱接入。我们使用深增强学习设计了基于经验累积（GoEA）的全局优化算法。在GOEA中，提出了一种与经常性神经网络和深Q网络相结合的网络结构，用于学习时变过程，然后应用奖励方法来优化全球实用程序。需要传输消息的车辆在Goea的指导之后动态地选择通道。实验结果表明，G跳协议将分组损耗率降低0.8至约0.1。同时，与开槽Aloha和DQN相比，我们的GoA算法将碰撞概率和通道空转概率降低了60％。此外，随着车辆的数量增加，传输成功率可以提高20％。

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