• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Annals of telecommunications >Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach
【24h】

Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach

机译：用于车辆通信的网络切片：多代理深度加强学习方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper studies the multi-agent resource allocation problem in vehicular networks using non-orthogonal multiple access (NOMA) and network slicing. Vehicles want to broadcast multiple packets with heterogeneous quality-of-service (QoS) requirements, such as safety-related packets (e.g., accident reports) that require very low latency communication, while raw sensor data sharing (e.g., high-definition map sharing) requires high-speed communication. To ensure heterogeneous service requirements for different packets, we propose a network slicing architecture. We focus on a non-cellular network scenario where vehicles communicate by the broadcast approach via the direct device-to-device interface (i.e., sidelink communication). In such a vehicular network, resource allocation among vehicles is very difficult, mainly due to (i) the rapid variation of wireless channels among highly mobile vehicles and (ii) the lack of a central coordination point. Thus, the possibility of acquiring instantaneous channel state information to perform centralized resource allocation is precluded. The resource allocation problem considered is therefore very complex. It includes not only the usual spectrum and power allocation, but also coverage selection (which target vehicles to broadcast to) and packet selection (which network slice to use). This problem must be solved jointly since selected packets can be overlaid using NOMA and therefore spectrum and power must be carefully allocated for better vehicle coverage. To do so, we first provide a mathematical programming formulation and a thorough NP-hardness analysis of the problem. Then, we model it as a multi-agent Markov decision process. Finally, to solve it efficiently, we use a deep reinforcement learning (DRL) approach and specifically propose a deep Q learning (DQL) algorithm. The proposed DQL algorithm is practical because it can be implemented in an online and distributed manner. It is based on a cooperative learning strategy in which all agents perceive a common reward and thus learn cooperatively and distributively to improve the resource allocation solution through offline training. We show that our approach is robust and efficient when faced with different variations of the network parameters and compared to centralized benchmarks.
机译：本文研究了使用非正交多次访问（NOMA）和网络切片车辆网络中的多代理资源分配问题。车辆想要广播具有异构质量（QoS）要求的多个数据包，例如需要非常低延迟通信的安全相关的数据包（例如，事故报告），而原始传感器数据共享（例如，高清地图共享） ）需要高速通信。为确保不同数据包的异构服务要求，我们提出了一种网络切片架构。我们专注于非蜂窝网络场景，其中车辆通过直接设备到设备接口（即，Sidelink通信）通过广播方法进行通信。在这种车辆网络中，车辆之间的资源分配非常困难，主要是由于（i）高度移动车辆之间的无线通道的快速变化，并且（ii）缺乏中心协调点。因此，排除了获取瞬时信道状态信息以执行集中资源分配的可能性。因此，所考虑的资源分配问题非常复杂。它不仅包括通常的频谱和功率分配，还包括覆盖选择（以播放的目标车辆）和数据包选择（使用哪个网络切片）。必须共同解决这个问题，因为可以使用NOMA覆盖所选择的分组，因此必须仔细分配频谱和电力以获得更好的车辆覆盖。为此，我们首先提供了一种数学编程配方和彻底的NP硬度分析问题。然后，我们将其模拟为多代理马尔可夫决策过程。最后，为了有效地解决它，我们使用深度加强学习（DRL）方法，具体提出深度Q学习（DQL）算法。所提出的DQL算法实用，因为它可以以在线和分布式方式实现。它基于合作学习策略，其中所有代理商都感知共同奖励，从而通过离线培训来协同和经济地学习，以改善资源分配解决方案。我们表明，当面对网络参数的不同变体并与集中基准相比，我们的方法是强大而有效的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号